Why Upskilling is Crucial for GenAI Adoption Across the Organization

Upskilling is essential across all teams when it comes to adopting Generative AI (GenAI). The success or failure of developing a GenAI application hinges on effective resource utilization. Training large language models (LLMs) can take weeks or even months, requiring significant computing infrastructure, including GPU and TPU units. Companies often pass these high costs onto consumers to remain profitable.

This situation is comparable to the IT industry, where specialists in security are paid more due to their expertise. Similarly, LLMs, which are designed to "know everything" akin to human intelligence, can command higher costs per token. Both operate on deep learning transformer architectures, which function much like neurons in the human brain.

Even when incorporating design patterns into GenAI applications, such as caching and storage solutions, the associated costs can still grow substantially due to the increased demand for storage and retrieval. Therefore, effective upskilling is crucial to avoid cost inefficiencies.

In the cloud computing world, I have personally observed instances where resources are either over scaled and underutilized or under scaled, leading to poor application performance. This can result in customers leaving or not using the app.
It is imperative to architect GenAI applications with a focus on cost optimization while ensuring that security and performance are not compromised. Strategic planning is required to scale efficiently and manage costs effectively.

Furthermore, it is crucial for every organization to have well-experienced Data Scientists, especially when working with Generative AI technologies like Retrieval-Augmented Generation (RAG). There may be situations where RAG does not produce accurate results or fails to reduce hallucinations. In such cases, fine-tuning the model becomes necessary, which also incurs additional costs.

Fine-tuning involves retraining the model to better suit specific needs, requiring the preparation of datasets and the maintenance of MLOps (Machine Learning Operations). This process can be resource-intensive, but it is essential for improving model performance and ensuring that the AI meets organizational objectives.

Financial based sectors the Data privacy and Security is two important Eyes we need to take care along with AI, CI, BI and DI.
While open-source models like Meta can offer significant cost savings, organizations must carefully evaluate their suitability in terms of quality and security. Investing in proper fine-tuning, security measures, and ongoing maintenance can help mitigate some of these risks, ensuring that the model meets the organization's needs without compromising on quality or security.

"Large Language Models (LLMs) are the cornerstone of Generative AI applications, much like the way a creator brings new forms to life. Just as the quality and intricacy of a sculptor’s work depend on their skills and vision, the effectiveness of GenAI applications depends on how well they are architected and designed. In this analogy, LLMs serve as the 'source' or 'engine' from which these applications emerge, but the final outcome varies based on how thoughtfully and skillfully the applications are crafted."
Although a full architecture of GenAI applications is similar to any other SaaS or Enterprise architecture on the cloud, however the key components Like LLM, Embeddings, Vector DB, Agent framework (LangChain, MS Semantic Kernel etc.) that make GenAI applications special.

Let us take a Vector DB component , how this impact on GenAI app development if we do not choose right DB.

Vector DB

**
Vector databases are crucial for advanced applications in recommendation systems, NLP, computer vision, and retrieval-augmented generation (RAG) with LLMs due to their ability to perform ultra-fast similarity searches on high-dimensional data. When choosing a vector database, it's essential to benchmark various performance aspects:
• Index Building Time: Measures the speed of creating search indexes.
• Insertion Throughput: Tracks how quickly new vectors are added.
• Search Latency: Assesses the response time for search queries.
• Search Throughput: Evaluates the number of queries processed per second.
• Scalability: Tests performance across different dataset sizes and loads.
• Accuracy: Measures precision and recall of search results.
• Resource Efficiency and Cost: Compares performance against resource use and operational costs.
Benchmarking should consider these factors to select the most suitable database for your needs, balancing performance and cost. Here I am going to compare Azure AI Search and PostgreSQL PgVector
Azure AI Search, also known as Azure Cognitive Search, it’s a cloud-based search service provided by Microsoft Azure. Here’s a comparison specifically between Azure Cognitive Search and PostgreSQL Vector (PgVector):

Azure Cognitive Search

Definition: Azure Cognitive Search is a fully managed search-as-a-service solution on Microsoft Azure that provides powerful search capabilities, including full-text search, semantic search, and vector search. It integrates AI capabilities to enhance search relevance and user experience.
Key Features:
• Full-Text and Semantic Search: Supports sophisticated search functionalities, including full-text search, semantic search, and built-in AI-driven search enhancements.
• Vector Search: Includes vector search capabilities for finding similar items based on embeddings.
• Scalability: Automatically scales to handle large datasets and high traffic with managed infrastructure.
• AI Enrichment: Offers AI-driven features like entity recognition, language detection, and image analysis.
• Indexing and Querying: Provides powerful indexing and querying capabilities with support for complex search scenarios.
Advantages:
• Managed Service: Reduces the overhead of managing infrastructure and scaling.
• Advanced Search Capabilities: Includes advanced features such as semantic search and AI enrichment that go beyond simple keyword search.
• Integration with Azure Ecosystem: Seamless integration with other Azure services and tools.
• Ease of Use: Provides a user-friendly interface and API for easy integration and management.
Use Cases:
• Enterprise search applications.
• E-commerce product search and recommendation systems.
• Knowledge management and document search.
• Applications requiring advanced AI-driven search features.
PostgreSQL Vector (PgVector)
Definition: PgVector is an extension for PostgreSQL that adds support for vector embeddings, enabling similarity search and other vector-based operations directly within a PostgreSQL database.
Key Features:
• Vector Storage and Search: Enables storage and querying of vector embeddings using distance metrics like cosine similarity or Euclidean distance.
• SQL Integration: Allows combining vector search with traditional SQL queries, leveraging PostgreSQL’s relational database features.
• Transactional Support: Benefits from PostgreSQL’s transactional support, data integrity, and ACID properties.
Advantages:
• Unified Database: Integrates vector search with relational data in a single database system.
• Flexibility: Provides the ability to perform vector searches alongside traditional SQL queries.
• Lower Cost: Can be cost-effective if using existing PostgreSQL infrastructure without the need for additional services.
Use Cases:
• Applications requiring both relational data and vector search capabilities.
• Projects where integrating vector search into an existing PostgreSQL setup is preferable.
• Moderate-scale applications where PostgreSQL’s capabilities are sufficient.

Comparison

1. Specialization:
   o Azure Cognitive Search: Highly specialized for advanced search scenarios with a broad range of AI-driven features.
   o PgVector: Adds vector search capabilities to PostgreSQL, integrating with relational data but with less specialization in search features.

2. Scalability:

o Azure Cognitive Search:
Designed to scale automatically and handle large datasets and high traffic.
o PgVector:
Scales with PostgreSQL’s capabilities; may require manual scaling and management.

3.** Ease of Use:**

o Azure Cognitive Search:
Managed service with a user-friendly interface and API, reducing the need for infrastructure management.
o PgVector:
Integration with PostgreSQL provides ease of use for those already familiar with SQL, but may involve more manual setup and management.

1. Performance:

o Azure Cognitive Search:
Optimized for high performance in search scenarios, including vector search.
o PgVector:
Performance depends on PostgreSQL’s indexing and query capabilities; may not match the specialized search service for very large datasets.

1. Cost:

o Azure Cognitive Search:
Costs are based on service usage and can vary depending on the scale and features used.

o PgVector:
Costs are related to PostgreSQL infrastructure; potentially lower if using existing resources.

In summary, Azure Cognitive Search is a powerful, managed search service with advanced AI and vector search capabilities, ideal for large-scale and complex search scenarios. PgVector, on the other hand, provides vector search capabilities within PostgreSQL, suitable for integrating with existing relational data and applications with moderate search needs.