DEV Community: Abbas Sibai

From SQL to Conversation: Exploring Oracle Select AI

Abbas Sibai — Wed, 16 Sep 2026 03:17:15 +0000

Introduction

What if you could query your Oracle database without writing SQL?
Oracle Select AI brings generative AI directly into the database, allowing users to ask questions in natural language and receive answers, tables, summaries, and even visualizations.
In this article, we'll explore what Select AI is, how it works, how it compares to traditional querying methods, and why it has the potential to make data more accessible to both technical and business users.

From SQL to Conversation: Exploring Oracle Select AI

For decades, interacting with databases has required a common language: SQL.
Whether you wanted to retrieve employee records, calculate sales totals, or analyze business trends, you needed to understand database structures and write queries that accurately expressed your request.
While SQL remains one of the most powerful technologies in computing, it creates a barrier for many users. Business analysts, managers, HR specialists, and executives often know exactly what information they need but may not know how to write the query required to retrieve it.
And This is where Oracle Select AI comes in.
Select AI introduces natural language interactions directly within Oracle Database, allowing users to ask questions in plain English while Oracle automatically generates and executes the appropriate SQL behind the scenes.
Instead of writing SQL queries such as:

SELECT department_name,
COUNT(*)
FROM employees
GROUP BY department_name
ORDER BY COUNT(*) DESC;

A user can just simply ask:
Which departments have the most employees?
Oracle translates the request into SQL, executes it, and returns the results.
The experience feels less like querying a database and more like having a conversation with your data

What Is Oracle Select AI?

Oracle Select AI is an Oracle Database capability that integrates large language models (LLMs) with database querying.
It enables users to:
• Ask questions using natural language
• Automatically generate SQL
• Retrieve structured answers from enterprise data
• Summarize results
• Generate charts and visual insights
• Interact with data conversationally

The key difference is that users no longer need to understand database schemas, table relationships, or SQL syntax to retrieve information.

Traditional SQL vs Select AI

Instead of focusing on:
Which tables contain employee information?
Users can focus on:
Show me employees hired this year.

How Select AI Works

Step 1: User Asks a Question

Example:
What were our top-selling products last quarter?

Step 2: AI Understands the Intent

The large language model analyzes:
• Business terminology
• Database schema
• Relationships between tables
• User context

Step 3: SQL Is Generated
Oracle automatically creates a SQL statement designed to answer the question.

Step 4: Results Are Returned
The user receives:
• Data tables
• Summaries
• Insights
• Charts and visualizations
All without manually writing SQL.

Why This Matters

One of the biggest challenges organizations face is data accessibility even though data,reports and dashboards already exist.

But users still need technical teams to answer simple questions.

Typical requests often look like:

Can someone run a report for all active employees?
Which department had the highest turnover this year?
Which supplier generated the most revenue? With Select AI, many of these questions can potentially be answered immediately by the end user.

This reduces dependency on technical resources and enables faster decision-making.

Select AI and Oracle Autonomous AI Database 26ai

A particularly exciting aspect of Select AI is how it fits into Oracle's broader AI strategy.
The Oracle Autonomous AI Database 26ai combines:
• Relational data
• JSON documents
• Graph data
• Vector data
• Artificial intelligence
Within this ecosystem, Select AI acts as the conversational layer that allows users to interact with all these data types more naturally.
As databases evolve from simple storage systems to intelligent platforms, natural language access will likely become as important as SQL itself.

Final Thoughts

Oracle Select AI represents a significant shift in how users interact with data. Instead of learning database structures and SQL syntax, users can focus on asking business questions and receiving meaningful answers.
SQL isn't going away anytime soon. Technical professionals will still need it for development, tuning, and advanced analytics. However, Select AI lowers the barrier to entry and opens the door for a much broader audience to explore and understand enterprise data.
As generative AI continues transforming enterprise applications, Oracle Select AI demonstrates how AI can move beyond content generation and become a practical tool for unlocking the value hidden within our databases.
The future of analytics may not start with a SQL statement. It may start with a question.

Oracle HCM Position Hierarchy Explained: A Beginner's Guide to Structure, Setup, and HDL

Abbas Sibai — Mon, 14 Sep 2026 19:14:22 +0000

Introduction

I recently came across the concept of Position Hierarchies in Oracle HCM and, at first, I wasn't entirely sure why they were needed.

After all, Oracle HCM already has managers and reporting relationships. If employees can report to managers, why do we need another hierarchy?

As I dug deeper, I realized that Position Hierarchies solve a different business problem. They focus on the organizational structure itself rather than the individuals occupying roles.

In this article, I'll explain what Position Hierarchies are, why organizations use them, what setup is required, and how they can be loaded using HDL.

Instead of saying:

Employee A reports to Employee B

Position Hierarchy says:

Position A reports to Position B

This allows organizations to maintain a stable structure even when employees leave or move between positions.

What Is a Position?

A Position represents a specific role or slot within an organization.
A worker may be assigned to a position, but the position itself exists independently of any individual employee.

For example:

Even if the Finance Manager leaves the company, the position remains available for a future employee.

What Is a Position Hierarchy?

A Position Hierarchy defines the parent-child relationships between positions.

Example:

The hierarchy is built using positions rather than workers. And workers simply occupy those positions.

Why Do Organizations Use Position Hierarchies?

Position Hierarchies provide several advantages like :

Workforce Planning

Organizations can easily visualize their organizational structure and future growth plans.

Vacancy Management

The reporting structure remains intact even when positions become vacant.

Approval Routing

Approvals can be routed through the position structure rather than relying solely on manager relationships.

Security

Certain security and data access models can leverage hierarchy information.

Headcount Management

Organizations can track authorized positions separately from actual employees.

Position Hierarchy vs Manager Hierarchy

Manager Hierarchy

The hierarchy changes when people move or leave.

Position Hierarchy

The hierarchy remains unchanged even when different employees occupy these positions.

Before building a Position Hierarchy, several foundational configurations should already exist:

1. Enterprise Structure Setup

The organization should already have the following configured within Oracle HCM.:

Legal Employers
Business Units
Departments
Locations

2. Enable Position Management

Position Management should be enabled.

Navigation:

Setup and Maintenance→ Manage Enterprise HCM Information

Enable: Use Position Management

This allows positions to become a central part of workforce management.

3. Create Positions

Positions must exist before they can participate in a hierarchy.

Configuration Steps

Step 1: Create Positions

Navigation:

My Client Groups→ Workforce Structures→ Positions
Create all required positions first.

Step 2: Define Parent Positions

When creating or updating a position, define its parent position.

Step 3: Build the Position Hierarchy

Oracle uses these parent-child relationships to construct the hierarchy.

Step 4: Run Hierarchy Processes

After large data loads or hierarchy updates, run the appropriate hierarchy-related processes so that reporting and approvals recognize the latest structure.

HDL Loads Required

In real implementations, organizations often create hundreds or thousands of positions.

Because of this, HDL is commonly used.

HDL Load 1: Positions

Create the positions.

HDL Load 2: Parent Position Relationships

Include the parent position information.

A replacement worker can later occupy the position without requiring any changes to the organizational structure if a position was to become vacant.

> This is one of the biggest advantages of Position Hierarchies.

Common Implementation Mistakes

Parent Positions Not Loaded First

A child position cannot reference a parent position that does not exist.

Always create parent positions first.

Circular Hierarchies

Avoid configurations such as:

Position A → Position B
Position B → Position A

These relationships are invalid.

Assuming Vacancies Break the Hierarchy

A vacant position does not impact the hierarchy.

The position remains part of the organizational structure.

Forgetting Hierarchy Processes

After significant hierarchy updates through HDL, hierarchy processes may need to be run to refresh reporting structures and approvals.

Final Thoughts

Position Hierarchies provide a powerful way to model organizational structures in Oracle HCM. Unlike Manager Hierarchies, they focus on positions rather than individuals, creating a stable framework that survives employee movement, promotions, transfers, and vacancies.

By enabling Position Management, creating positions, defining parent-child relationships, and loading structures through HDL, organizations can build a scalable workforce model that supports planning, security, approvals, and reporting.

The key concept to remember is simple:

Manager Hierarchy = People report to people
Position Hierarchy = Positions report to positions

Workers may come and go, but the organizational structure remains unchanged. That stability is what makes Position Hierarchies such a valuable feature in Oracle HCM.

Oracle Autonomous AI Database 26ai: More Than Just a Database

Abbas Sibai — Fri, 11 Sep 2026 19:32:10 +0000

Recently, I completed the Oracle Autonomous AI Database 26ai learning path, and it gave me a great perspective on how databases are evolving in the AI era.

For years, databases were primarily viewed as systems for storing and retrieving data. Today, however, organizations expect much more. They want databases that can support artificial intelligence, machine learning, analytics, application development, and automation, all while reducing operational overhead. This is where Oracle Autonomous AI Database 26ai stands out.

From Traditional Databases to AI-Powered Databases

In a traditional environment, building an AI solution often requires multiple technologies working together:

A database for storing data
A vector database for semantic search
Analytics tools for reporting
Machine learning platforms for predictions
Application development platforms for user interfaces

However managing all these components can become complex and expensive.

Oracle's approach is different. Autonomous AI Database combines many of these capabilities into a single platform, allowing organizations to manage data, build applications, analyze information, and develop AI-powered solutions without stitching together multiple specialized products.

Think of it as the difference between carrying several separate tools versus having a multi-tool that includes everything you need in one place.

What Does "Converged Database" Really Mean?

One concept that was emphasized throughout the learning path was the idea of a converged database.

Traditionally, organizations might store different types of data in different systems:

With Oracle Autonomous AI Database, these data types can be managed within a single database.

This means developers can work with all the below without moving data between multiple technologies.

Relational data
JSON documents
Graph data
Spatial data
Vector embeddings

For organizations adopting AI, this can significantly simplify architecture and reduce data silos.

The Database That Manages Itself

One of the most impressive features is the autonomous capability.

Anyone who has worked with databases knows there are many administrative tasks involved:

Applying patches
Performing backups
Monitoring performance
Managing storage
Implementing security updates

Traditionally, these tasks require dedicated database administrators and careful planning.

With Autonomous AI Database, many of these operations are automated.

Instead of spending time on routine maintenance, teams can focus more on innovation, analytics, and solution development.

This shift reminded me of how cloud services transformed infrastructure management. Rather than maintaining physical servers, organizations now consume infrastructure as a service. Autonomous Database applies a similar philosophy to database administration.

Oracle AI Vector Search: A Technology Worth Understanding

One of the highlights of the learning path was Oracle AI Vector Search.

Many people interact with AI applications daily through tools like ChatGPT, Copilot, and AI-powered search engines, but fewer understand what happens behind the scenes.

Traditional searches rely on exact keywords.

For example:

Searching for:

"Employee annual leave"

might not find a document titled:

"Vacation policy"

even though both discuss similar concepts.

Vector Search addresses this challenge by searching based on meaning rather than exact wording.

Information is converted into numerical representations called embeddings, allowing the database to compare semantic similarity.

This is one of the core technologies behind:

Retrieval-Augmented Generation (RAG)
AI assistants
Enterprise knowledge search
Intelligent recommendations

What I found particularly interesting is that Oracle has integrated vector capabilities directly into the database rather than requiring a separate vector database.

For organizations already using Oracle technologies, this can simplify AI adoption considerably.

Understanding Graph Data Through Real Relationships

Another area I enjoyed exploring was Graph Data.

Most traditional databases focus on rows and columns.

Graphs focus on relationships.

For example, in an organization:

Employee → Reports To → Manager
Manager → Reports To → Director
Director → Reports To → Vice President

Finding complex relationships becomes much easier using graph structures.

This approach has applications in:

Organizational hierarchies
Fraud detection
Social networks
Supply chain analysis
Recommendation engines

As AI solutions become more sophisticated, understanding relationships between entities becomes increasingly valuable.

Oracle Data Studio: Turning Data into Insights

Having data is one thing.

Understanding it is another.

The learning path introduced Oracle Data Studio tools for exploring and visualizing information.

These tools allow users to:

Load datasets
Explore information
Create visualizations
Build dashboards
Generate business insights

What stood out to me was how quickly users can move from raw data to meaningful visual analysis.

Instead of exporting data into multiple tools, much of the work can be performed within the Oracle ecosystem.

AI Isn't Just About Models

When people hear the term AI, they often think exclusively about machine learning models.

However, successful AI solutions require much more:

Data management
Data quality
Search capabilities
Security
Application interfaces
Automation

This learning path reinforced the importance of having a strong data foundation before implementing AI initiatives.

Even the most advanced AI models are only as effective as the data supporting them.

Oracle Select AI: Making Data Accessible with Natural Language

One area of the learning path that particularly caught my attention was Oracle Select AI.

Traditionally, accessing information from a database requires knowledge of SQL. Business users often depend on developers or data analysts to write queries, validate results, and build reports. Even seemingly simple questions can require complex joins, filters, and aggregations.

For example, a user may want to know:

Which department had the highest employee turnover last year?

Or:

Show me the top 10 products by revenue this quarter.

Without AI, answering these questions typically involves writing SQL, understanding table relationships, and validating the results.

With Oracle Select AI, users can interact with the database using natural language instead of SQL. Oracle automatically interprets the request, generates the appropriate query, executes it against the database, and returns meaningful results.

This significantly lowers the barrier between business users and data.

Building Applications with Oracle APEX

Another valuable component I was introduced to was Oracle APEX.

One common challenge organizations face is converting data and insights into usable business applications.

APEX provides a low-code approach to application development, allowing developers and technical users to create applications directly on top of Autonomous Database.

This can significantly reduce development effort while accelerating delivery of business solutions.

APEX has shown to be a very low code/cost way to develop customized applications, implementing any new business logic on Oracle screens as well as using the Autonomous Database. Thus there would be no need to provision a private database.

My Biggest Takeaways

After completing the learning path, several themes stood out:

1. Data and AI Are Becoming One Platform

Rather than treating AI as a separate technology stack, Oracle is embedding AI capabilities directly into the database.

2. Vector Search Will Become Increasingly Important

As organizations adopt Generative AI solutions, vector search is rapidly becoming a foundational technology.

3. Simplicity Matters

Managing multiple databases and tools can introduce complexity. A converged approach helps reduce architectural overhead.

4. Automation Is Changing Database Administration

Routine operational tasks are becoming increasingly automated, allowing teams to focus on higher-value work.

5. Modern Developers Need Broader Skills

Today's professionals benefit from understanding not only SQL and databases, but also AI, analytics, vector search, graph technologies, and low-code development.

Final Thoughts

The Oracle Autonomous AI Database 26ai learning path was much more than a database course. It provided a practical introduction to how modern data platforms are evolving to support AI-powered applications.

Whether you're a database administrator, developer, data engineer, architect, or someone exploring AI technologies, understanding concepts such as converged databases, vector search, graph analytics, machine learning, and autonomous operations will become increasingly valuable.

For me, the biggest takeaway was simple: the future of AI starts with data, and the database is no longer just a place to store information. It is becoming an intelligent platform for innovation.

Understanding the Oracle HCM worker ecosystem for successful conversions

Abbas Sibai — Wed, 09 Sep 2026 19:31:27 +0000

A lesson I learned the hard way:

When I started working on Oracle HCM conversions, I assumed that loading workers would be one of the simpler parts of the project. Every HR system has employees, so the process seemed straightforward: extract the employee information, transform it into HDL, load it into Oracle, and move on.

That assumption changed during an early conversion cycle. I was troubleshooting a worker load that looked correct. The syntax was valid, the person number was present, and the main dates seemed aligned. Yet the result still was not what I expected.

The turning point came when I stopped treating the worker as one record. Oracle was not failing to understand an employee row. Oracle was validating a network of connected business objects, each with its own keys, dates, reference values, and dependencies.

The mindset shift: I was not loading a worker. I was loading the person, the employment relationship, the assignment, the workplace structure, and the relationships surrounding that assignment.

A worker is an ecosystem :

In a source system, the employee may appear in one extract with a name, phone, email, department, job, position, manager, and salary. In Oracle Fusion HCM, those details do not all belong to the same object.

A practical Core HR conversion can involve the following connected records:

This structure is why a Worker.dat file can be technically valid and still fail, or load successfully while producing an incomplete business result.

The hierarchy that changed how I troubleshoot

Department, job, and position are not separate worker children

Once I started visualizing the worker this way, troubleshooting became more focused. Instead of asking, “Why did the worker fail?” I started asking, *“Which dependency, date, or reference value is missing?” *

Department, job, and position are important to the worker story, but conceptually they belong to the worker’s assignment and the workplace structure referenced by that assignment. They describe where the worker is organized, the type of work being performed, and the specific position the worker occupies.

Why this matters? A worker may be ready to load, but the assignment can still fail when a referenced department, job, position, business unit, legal employer, grade, location, or person type is not available or does not match the HDL value.

The Assignment object is where the pieces meet

The Assignment object is where the worker begins to look like part of an organization. It connects the employment record to organizational and workforce structures.

This is one reason I now consider reference-data validation part of the conversion design, not merely an error-resolution activity. Before generating the final HDL file, the project should know whether the master data assignment values exist in the target environment and whether the mappings have been approved.

Manager relationships add a date problem

Loading a manager initially sounds straightforward: identify the worker, identify the manager, and create the supervisor relationship. The complexity appears when the worker assignment and manager assignment were not effective on the same date.

A manager relationship must point to valid worker and manager assignments and must be effective on a date supported by the assignment history. Depending on the scenario, the conversion may require a date-effective assignment transaction in addition to the Assignment Supervisor record.

That experience made manager effective dates one of my early conversion checks:

Worker assignment start date
Manager assignment start date
Intended supervisor relationship date
Effective sequence when multiple assignment changes occur on the same day

Salary depends on the assignment too

Salary is another good example of why the worker should be viewed as an ecosystem. The salary record does not stand alone. It is associated with the worker’s assignment, and the relevant salary basis must already be configured. The salary start date also needs to make sense in relation to the assignment history.

This same dependency pattern repeats across HCM conversions: one object provides the business information, while another object provides the foundation that makes the information valid.

Final thoughts

One of the most useful lessons Oracle HCM conversions taught me is that a worker is much more than a person record.

Behind every worker is a collection of person information, employment relationships, workplace structures, assignments, managers, compensation details, contacts, effective dates, and source keys. Every piece helps Oracle understand the complete workforce story.

Once that structure becomes clear, mapping becomes more intentional, troubleshooting becomes faster, and validation becomes more meaningful.

Oracle Cloud Infrastructure AI Foundations: A Step into the World of AI and Machine Learning

Abbas Sibai — Tue, 08 Sep 2026 21:09:28 +0000

When people hear the term artificial intelligence, they often think of tools that generate text, create images, or answer questions. But artificial intelligence is much broader than that.

It includes systems that can recognize objects in images, understand spoken language, analyze documents, identify patterns in data, make predictions, and help organizations automate complex tasks.

After completing the Oracle Cloud Infrastructure AI Foundations course, I gained a clearer understanding of how these technologies connect and how organizations can use them through Oracle Cloud Infrastructure.

The course does not focus only on Generative AI. It builds the foundation first by explaining artificial intelligence, machine learning, deep learning, neural networks, and language models before introducing Oracle’s AI services.

Oracle University describes the OCI AI Foundations training as an introduction to AI and machine learning concepts, including common AI terminology, Generative AI, and Large Language Models.

As someone who works with Oracle technologies, databases, reporting, integrations, and enterprise applications, I found the course especially valuable because it connected modern AI concepts to Oracle’s broader cloud and database ecosystem.

Some of the key concepts I learned were related to understanding the differences between AI, Machine Learning, and Deep Learning.

These terms are often used interchangeably, but they represent different layers of technology.

Artificial Intelligence

Artificial intelligence is the broadest concept of all the others.

It refers to systems designed to perform tasks that normally require human intelligence, such as:

Understanding language
Recognizing images
Making decisions
Solving problems
Identifying patterns
Generating content

AI is the overall field that contains machine learning, deep learning, Generative AI, computer vision, natural language processing, and several other areas.

Machine Learning

Machine learning is a subset of artificial intelligence.

Rather than programming every rule manually, we provide a machine learning algorithm with data so it can identify patterns and learn how to make predictions.

For example, imagine that an organization wants to predict whether employees are likely to leave.

A traditional application might require developers to create rules such as:

Flag employees with low job satisfaction.
Flag employees who have not received a promotion.
Flag employees working excessive overtime.

A machine learning model takes a different approach.

It analyzes historical employee information and learns which combination of factors is associated with employee turnover.

The model can then use those patterns to make predictions about new cases.

Deep Learning

Deep learning is a specialized area of machine learning that uses artificial neural networks with multiple layers.

These networks are inspired, at a simplified level, by how the human brain processes information.

Deep learning is especially useful when working with complex and unstructured data, including:

Images
Audio
Video
Natural language
Large document collections

A simple way to visualize the relationship is:

This distinction helped me understand that Generative AI did not appear out of nowhere. It is the result of decades of development across AI, machine learning, neural networks, and natural language processing.

How Machine Learning Learns from Data

The course also explores the main ways machine learning models learn.

Two of the most important approaches are supervised learning and unsupervised learning.

Supervised Learning

In supervised learning, the model learns from labelled examples.

The training data contains both the input information and the expected answer.

For example, imagine a dataset containing previous expense reports:

Because the historical result is available, a model can learn which patterns are normally associated with an approval or additional review.

Supervised learning is frequently used for:

Classification
Fraud detection
Risk prediction
Forecasting
Customer churn analysis
Sentiment analysis

Classification predicts a category, such as whether an email is spam.

Whereas regression predicts a numerical value, such as next month’s sales.

Unsupervised Learning

In unsupervised learning, the data does not include a predefined answer.

Instead, the algorithm looks for hidden patterns or natural groupings within the data.

For example, a company may have thousands of customers but no predefined customer categories.

An unsupervised learning algorithm could examine customer behaviour and discover groups such as:

Frequent high-value customers
Occasional seasonal customers
Customers at risk of becoming inactive
New customers with rapid growth

This technique is often called clustering.

Unsupervised learning can be useful for:

Customer segmentation
Anomaly detection
Pattern discovery
Document grouping
Recommendation systems

The key lesson is that selecting the right learning approach depends on the business problem and the type of data available.

Exploring Deep Learning Architectures

The course also introduces several important neural network architectures, including CNNs, RNNs, and LSTMs.

At first, these names can sound highly technical. However, each architecture is designed to handle a particular type of problem.

Convolutional Neural Networks

Convolutional Neural Networks, commonly known as CNNs, are especially effective for image-related tasks. CNN processes an image by identifying visual features in stages.

The earlier layers may recognize simple elements such as:

Lines
Edges
Colors
Shapes

Deeper layers combine these elements to identify more complex objects.

CNNs can support use cases such as:

Product defect detection
Medical image analysis
Facial recognition
Document classification
Object detection
Inventory monitoring

For example a manufacturing company could use a CNN to review product images and detect defects before products leave the facility.

Recurrent Neural Networks

Recurrent Neural Networks, or RNNs, are designed to process sequential information. Sequence matters when the meaning of the current input depends on what came before it.

Examples include:

Sentences
Audio signals
Time-series data
Financial transactions
Sensor readings

An RNN maintains information about previous inputs while processing the current input. This makes it useful for understanding patterns that occur over time.

Long Short-Term Memory Networks

Long Short-Term Memory networks, or LSTMs, are a specialized type of recurrent neural network.

Traditional RNNs can struggle to retain information from much earlier in a long sequence. LSTMs use a memory structure that helps preserve important information for longer periods.

They have been used for:

Language translation
Speech recognition
Time-series forecasting
Text generation
Sequence classification

Learning about these architectures helped me understand why no single model works best for every AI problem.

The architecture must match the structure of the data and the outcome the organization wants to achieve.

Enter Generative AI

Traditional machine learning models often classify information or predict an outcome.

Generative AI goes one step further.

It creates new content based on patterns learned during training.

This content can include:

Text
Images
Audio
Code
Summaries
Answers
Recommendations

For example, a traditional model might classify a customer review as positive or negative.

A Generative AI model could summarize the review, identify the main concern, and draft a suggested response.

This introduces an important shift:

Traditional AI: Input → Prediction or classification
vs
Generative AI: Input → Newly generated content

Oracle defines a Generative AI model as one trained on large amounts of data that can accept new inputs and generate new content.

Understanding Large Language Models

Large Language Models, usually called LLMs, are AI models trained on large collections of text.

During training, an LLM learns relationships between words, sentences, concepts, and language patterns. At a basic level, an LLM generates text by predicting which token is likely to come next based on the prompt and the context it receives.

A token can be:

A complete word
Part of a word
A number
A punctuation mark
Another small unit of text

While next-token prediction sounds simple, training on a very large amount of data allows an LLM to perform sophisticated tasks such as:

Answering questions
Summarizing documents
Extracting information
Generating code
Translating languages
Classifying text
Supporting conversations

This also explains why prompts are so important.

The model generates its response based on the instructions and context we provide.

Prompt Engineering

Prompt engineering is the process of designing clear instructions that guide a language model toward a useful response.

For example, consider this basic prompt:

> Explain machine learning.

Now if we compare it with:

Explain machine learning to a business analyst with no technical
background. Use an employee retention example and keep the explanation
under 300 words.

The second prompt gives the model:

A target audience
A specific topic
A business context
A length requirement
A preferred level of complexity

Oracle describes prompt engineering as an iterative process of crafting natural-language requests to guide an LLM toward better or different outputs.

One of my takeaways was that using Generative AI effectively is not simply about asking more questions. It is about providing the right instructions, relevant context, and clear constraints.

The Challenge of LLMs

Large Language Models are powerful, but they are not perfect.

They can produce responses that sound convincing but are incorrect. This is commonly called hallucination.

They may also:

Lack access to current information
Lack access to private organizational data
Misunderstand an unclear prompt
Generate inconsistent answers
Provide information without reliable grounding

This is why enterprise AI requires more than a language model.

Organizations also need:

Secure access controls
Trusted business data
Model governance
Monitoring
Guardrails
Retrieval mechanisms
Human oversight

This is where Oracle’s broader AI stack becomes important.

OCI Generative AI

OCI Generative AI is a managed service for building and operating enterprise Generative AI applications.

Oracle’s current documentation describes it as a fully managed OCI service supporting tasks such as chat, embeddings, reranking, semantic search, classification, clustering, recommendations, and enterprise agents. It provides access to pretrained models while also supporting custom-model deployment options.

The service helps organizations move from experimenting with an LLM to building AI applications with stronger enterprise controls.

Potential use cases include:

Internal knowledge assistants
Document summarization
Customer service support
Content generation
Semantic search
Classification
Recommendation systems
Business process automation

In addition to that, OCI Generative AI also supports embeddings.

An embedding is a numerical representation of text that preserves aspects of its semantic meaning. Embeddings are frequently stored in vector databases and used for similarity searches based on context instead of exact keywords.

This creates an important connection between OCI Generative AI and Oracle AI Vector Search.

Oracle AI Vector Search

An LLM may be able to explain general concepts, but it normally does not know an organization’s internal policies, reports, procedures, or business records.

Oracle AI Vector Search helps applications retrieve relevant information based on meaning.

A keyword search might not recognize the connection between these phrases.

A vector search can compare their semantic meaning and identify that they are closely related.

Oracle AI Vector Search supports semantic queries over vector embeddings and allows unstructured information to be searched alongside relational business data.

This connection between enterprise data and semantic search is one of Oracle’s most valuable AI capabilities.

Retrieval-Augmented Generation

Retrieval-Augmented Generation, or RAG, connects information retrieval with a Generative AI model.

Instead of asking the model to depend only on knowledge learned during training, the application supplies relevant enterprise information at the time of the request.

Oracle describes RAG as a process that retrieves data from selected sources and uses it to augment an LLM response with grounded information.

For example, an HR assistant could retrieve:

Company leave policies
Onboarding procedures
Benefits documents
Internal support instructions

The language model could then generate an answer based on those approved sources.

This does not automatically eliminate every risk, but it can make the response more relevant, explainable, and connected to trusted organizational content.

OCI Vision

OCI Vision helps applications analyze images.

Instead of developing and training an image-recognition model from the beginning, developers can use a managed cloud service for common computer-vision requirements.

Potential business applications include:

Identifying objects in images
Classifying images
Detecting text
Inspecting products
Categorizing visual records
Supporting document processing workflows

For example, a retailer could analyze product images to improve catalog management, while a manufacturer could use computer vision to identify visible defects.

OCI Speech

OCI Speech converts spoken audio into text.

This can support:

Meeting transcription
Call-center analysis
Interview transcription
Subtitle generation
Voice-enabled applications
Searchable audio archives

Once audio is converted into text, the result can be summarized, classified, searched, or analyzed by other AI services.

This demonstrates how OCI services can be combined.

A meeting recording could be:

Transcribed with OCI Speech.
Analyzed for key topics.
Summarized using Generative AI.
Stored for later semantic search.
Retrieved through an internal knowledge assistant.
OCI Language

OCI Language helps applications analyze and understand text.

Potential capabilities and use cases include:

Sentiment analysis
Key phrase extraction
Entity recognition
Language detection
Text classification
Business feedback analysis

Imagine an organization receiving thousands of employee survey comments.

OCI Language could help identify:

Common themes
Frequently mentioned topics
Positive or negative sentiment
Departments or locations mentioned
Emerging areas of concern

This turns large volumes of unstructured text into information that can support business decisions.

OCI Document Understanding

Organizations often store important information in forms, invoices, receipts, contracts, and scanned documents.

The information may be visible to a person but difficult for a traditional system to process.

OCI Document Understanding can help extract and organize information from these documents.

Potential use cases include:

Invoice processing
Receipt analysis
Form extraction
Contract classification
Identity document processing
Business document automation

The business value is not simply reading a document.

The real value comes from turning unstructured document content into structured information that applications can use.

Oracle Select AI

Another capability covered in the learning journey is Oracle Select AI.

Select AI allows users to interact with database data using natural language.

The system can use Generative AI to interpret the request and work with the underlying database.

For business users, this can make enterprise information more accessible.

For technical professionals, it creates new responsibilities around:

Data security
Metadata quality
Query validation
Access control
Result accuracy
Responsible AI usage

Select AI does not remove the importance of SQL or data experts. Instead, it creates another interface through which approved users can interact with enterprise data.

Choosing the Right OCI AI Service

One of the most practical lessons from the course was learning that not every business problem requires a custom machine learning model or an LLM.

The appropriate service depends on the input and the desired outcome.

If the problem requires a prediction based on organization-specific historical data, a custom machine learning workflow may be more appropriate.

If the organization needs semantic retrieval across enterprise content, Oracle AI Vector Search may be required.

If the goal is to let users ask questions about database data in natural language, Select AI may be the better fit.

The important question is not:

> How can we use AI?

A better question is:

> What business problem are we trying to solve, and which AI capability is most appropriate for it?

A Practical Enterprise Example

Consider an organization that wants to build an intelligent employee-support assistant.

Employees ask questions such as:

How many vacation days do I have?
What is the parental leave policy?
How do I update my bank information?
What documents are required for onboarding?
Who should approve my request?

This solution could combine several Oracle technologies:

Employee Question
↓
OCI Generative AI interprets the request
↓
Oracle AI Vector Search retrieves relevant policies
↓
Oracle Database retrieves authorized employee data
↓
The LLM creates a grounded response
↓
Security controls ensure the employee sees only permitted data
Show more lines

If an employee uploads a form, OCI Document Understanding could extract its fields.

If the employee asks the question verbally, OCI Speech could convert it into text.

If feedback is collected afterward, OCI Language could analyze the sentiment.

This example shows why the course covers a portfolio of services rather than presenting AI as a single product.

Real business solutions often combine several capabilities.

My Biggest Takeaway

Before completing the OCI AI Foundations course, it was easy to view AI as a collection of separate technologies.

Machine learning was one topic. Generative AI was another.

Cloud infrastructure, databases, vector search, and document processing appeared to be separate areas.

The course helped me see how these pieces fit together.

A successful enterprise AI solution may require:

Infrastructure to run the workload
Quality data to support the model
Machine learning to identify patterns
Deep learning to process complex information
Generative AI to create a response
Vector search to retrieve relevant knowledge
AI services to process images, speech, language, and documents
Security and governance to protect the organization

The value comes from combining the right components for the right business problem.

Why This Course Is Valuable for Oracle Professionals

This course is a strong starting point for people who work with:

Oracle Cloud Infrastructure
Oracle Database
Oracle Fusion Applications
Analytics and reporting
Application development
Cloud architecture
Data engineering
Business process automation
Enterprise integrations

AI will increasingly influence how users interact with applications and data.

Reports may become conversational, search may become semantic, and documents may become automatically processable.

Enterprise applications may include assistants that can retrieve trusted information and guide users through business processes.

Understanding the foundations makes it easier to evaluate these changes realistically rather than treating AI as only a trend.

Final Thoughts

The Oracle Cloud Infrastructure AI Foundations course provides more than an introduction to AI terminology.

It explains how artificial intelligence developed from machine learning and deep learning, how modern Generative AI and Large Language Models work, and how Oracle brings these capabilities into an enterprise cloud platform.

What I appreciated most was the structured learning journey.

The course begins with the fundamentals and gradually connects them to practical Oracle technologies, including:

OCI Generative AI
OCI AI Services
OCI machine learning capabilities
Oracle AI Vector Search
Oracle Select AI
Vision, Speech, Language, and Document Understanding

My biggest takeaway is that AI is not one model, service, or chatbot.

It is an ecosystem of technologies that must work together with business data, infrastructure, security, and governance.

Organizations do not gain value simply by adopting AI.

They gain value when they choose the right AI capability for a clearly defined problem, connect it to trusted data, and use it responsibly.

For Oracle professionals, this course provides an accessible entry point into that rapidly evolving landscape.

The future of enterprise technology is not only about storing information or automating transactions.

It is about helping people understand information, interact with it naturally, and use it to make better decisions.

Oracle AI Vector Search Certified Professional: My Journey into the Future of AI-Powered Databases

Abbas Sibai — Sun, 30 Aug 2026 03:29:09 +0000

When most people think of artificial intelligence, they picture chatbots, large language models, or tools that generate text and images. Databases rarely make that list.

Yet, after completing the Oracle AI Vector Search Certified Professional learning path, I came away with a completely different perspective:

The future of AI isn't just about generating answers. It's about finding the right information first.

And that is exactly where Oracle AI Vector Search shines.

As someone who has spent much of my career working with Oracle technologies, data, reporting, integrations, and HCM systems, I found this certification particularly exciting because it demonstrates how databases are evolving beyond simply storing data. They are becoming active participants in modern AI architectures.

What impressed me most was seeing how Oracle Database 23ai brings vector search, semantic understanding, Retrieval-Augmented Generation (RAG), and natural language querying directly into the database.

Let's explore what that actually means.

Why Traditional Search Isn't Enough

Imagine searching a knowledge base for:

"How do I onboard a new employee?"

Traditional search engines typically look for exact keywords.

If the document contains:

"New hire setup procedure"

it may not appear in the results because the wording doesn't match exactly.

Humans understand that both phrases mean almost the same thing.

Traditional databases do not.

Or at least they didn't.

Modern AI applications solve this problem by transforming information into vector embeddings, allowing systems to search by meaning rather than keywords.

Instead of asking:

"Does this text contain these words?"

we can ask:

"Which documents are most similar to what the user is looking for?"

That fundamental shift is what makes AI assistants feel intelligent.

Enter Oracle AI Vector Search

Oracle AI Vector Search introduces a native VECTOR data type within Oracle Database.

Rather than storing only rows and columns, organizations can now store vector embeddings alongside their business data.

Think of vector embeddings as coordinates in a multi-dimensional space.

Items with similar meanings are stored closer together.

A vector search can discover these relationships without requiring exact keyword matches.

This capability unlocks a completely new way of retrieving information.

The Secret Ingredient: Embeddings

One concept repeated throughout the certification was the importance of embeddings.

An embedding is simply a numeric representation of information.

For example:

Employee Onboarding Guide

might be transformed into something like:

[0.72, -0.31, 0.58, ...]

While these numbers mean little to humans, they capture the semantic meaning of the content.

The amazing part is that content discussing similar concepts will produce vectors located near one another.

This creates the foundation for intelligent search.

During the learning path, I explored how embeddings can be generated both inside and outside Oracle Database and then stored directly within Oracle's VECTOR data type.

Searching by Meaning, Not Words

One of my favorite topics was vector similarity search.

Instead of searching for matching text, Oracle can calculate how similar two vectors are.

The certification explores several methods including:

Cosine Similarity
Euclidean Distance
Manhattan Distance
Dot Product

A simple way to think about this is:

Imagine every document in your organization exists as a point on a giant map.

When a user asks a question, Oracle identifies the documents located closest to that question's vector representation.

Closer equals more relevant.

Further away equals less relevant.

This is the intelligence that powers modern recommendation systems, AI assistants, and semantic search engines.

HNSW and IVF:

The Engines Behind Fast Vector Search**

Finding similar vectors among millions of records sounds expensive.

That's where vector indexes come into play.

The certification introduces two important indexing strategies:

HNSW (Hierarchical Navigable Small World)

Think of HNSW as a network of interconnected roads.

Instead of checking every possible destination, Oracle can navigate quickly through connected paths to locate nearby vectors.

Benefits:

Extremely fast retrieval
High accuracy
Ideal for interactive AI applications

IVF (Inverted File Index)

IVF works differently.

It groups similar vectors into clusters.

When a search occurs, Oracle only checks the most relevant clusters rather than the entire dataset.

Benefits:

Efficient for very large datasets
Lower memory requirements
Highly scalable

Learning the strengths of each index type helped me better understand how organizations balance speed, accuracy, and infrastructure costs in production AI environments.

The Part Everyone Is Talking About:

Retrieval-Augmented Generation (RAG)**

If I had to choose one topic that generated the most excitement, it would be RAG.

Large Language Models are powerful but they have limitations.

They can:

Hallucinate
Use outdated information
Lack access to internal company knowledge

RAG addresses these issues by retrieving relevant information before generating an answer.

A typical flow looks like this:

Rather than guessing, the model responds using actual enterprise knowledge.

This results in answers that are:

More accurate
More trustworthy
More explainable

The certification demonstrates how Oracle supports building RAG solutions using both PL/SQL and Python, making it accessible to database professionals and developers alike.

Select AI:Speaking SQL Without Writing SQL**

Another incredibly interesting capability covered in the learning path is Oracle Select AI.

For example:

Show me employees hired last month.

Oracle can translate the request into SQL and execute it automatically.

This capability has the potential to make enterprise data more accessible to non-technical users while still leveraging the power of Oracle Database.

It may be one of the most practical applications of AI that organizations can adopt today.

Beyond Search:Enterprise-Scale AI**

The certification also explores how Oracle extends AI through technologies such as:

Exadata AI Storage

Oracle GoldenGate Distributed AI Processing
SQL Loader
Oracle Data Pump

What stood out to me here was Oracle's broader strategy.

Many vendors position AI as a separate platform.

Oracle is embedding AI directly into the database ecosystem that organizations already use to run critical business operations.

This significantly reduces complexity while enabling AI workloads to operate closer to enterprise data.

My Biggest Takeaway

Before taking this certification, I viewed Vector Search primarily as another AI feature.

After completing it, I now see it as one of the foundational building blocks of modern enterprise AI.

Organizations have spent decades collecting valuable information.

The challenge is no longer storing that information.

The challenge is finding the right information at the right time and using it effectively.

Vector embeddings, semantic search, RAG, and Select AI are all pieces of that puzzle.

And Oracle is positioning the database itself as the platform that brings those pieces together.

Final Thoughts

The Oracle AI Vector Search Certified Professional learning path is far more than a certification about vector databases.

It is an introduction to how modern AI systems retrieve knowledge, understand context, and generate relevant answers.

For database professionals, architects, developers, and anyone interested in Generative AI, it offers a practical look at the technologies powering the next generation of intelligent applications.

The future of AI is not simply generating content.

The future of AI is understanding meaning.

And Oracle AI Vector Search is helping make that future a reality.