DEV Community: Emmanuel Adegor

Gen AI vs LLM: Understanding the Core Differences and Practical Insights

Emmanuel Adegor — Tue, 07 Jan 2025 00:10:30 +0000

In the rapidly evolving field of artificial intelligence, two terms often make headlines: Generative AI (Gen AI) and Large Language Models (LLMs). While closely related, they are not synonymous. Understanding their differences, capabilities, and applications is crucial for developers, businesses, and AI enthusiasts.

Let’s explore what sets them apart, their intersections, and the art of prompt engineering — a vital skill in leveraging their potential.

Imagine Generative AI (Gen AI) as the human body, a complex, versatile system designed to perform multiple functions, creating and interacting with the world in various ways. Within this system, Large Language Models (LLMs) are like the senses (e.g., eyes for seeing, ears for hearing, or hands for writing). These senses are specialized tools that help the body accomplish specific tasks.

What is Generative AI?

Generative AI refers to systems designed to create new content from learned patterns, such as text, images, music, or even videos. These systems rely on advanced machine learning models, often trained on vast datasets, to “generate” creative and coherent outputs.

Key Features of Generative AI:

Innovative Content Creation: Generative AI excels at producing novel outputs, including synthetic images, realistic text, and lifelike audio, catering to a wide range of creative and practical applications.
Multi-Modal Expertise: Advanced tools such as DALL-E and MidJourney generate visually stunning images, while models like ChatGPT focus on crafting compelling text, enabling seamless integration across various content formats.
Tailored Outputs: Through fine-tuning, generative AI models can be customized to meet the unique needs of specific industries or use cases, ensuring precision and relevance in their outputs.
Diverse Applications: Generative AI finds utility in an array of fields, from crafting marketing content and enhancing storytelling in game development to enriching virtual reality experiences.

What are Large Language Models (LLMs)?

LLMs are a subset of generative AI focused on understanding and generating human-like text. These models are trained on billions of parameters and immense datasets, enabling them to perform language-related tasks with impressive accuracy.

Key Features of LLMs:

Text-based Tasks: LLMs handle translation, summarization, question answering, and conversational AI.
Pre-trained Models: Examples include OpenAI’s GPT series, Google’s BERT, and Meta’s LLaMA.
Contextual Understanding: LLMs predict text by understanding context, grammar, and semantics.
Scalable Intelligence: They are versatile, enabling use cases from code generation to legal document analysis.

Examples of Gen Ai:

DALL-E (images),
DeepArt (art generation)
ChatGPT (text).

Examples of LLMs:

GPT-4
BERT
T5
LLaMA

The Art of Prompt Engineering

Prompt engineering is the process of creating clear and effective questions or instructions to get the desired results from AI systems like Gen AI and LLMs. It’s a key skill for making the most out of AI interactions.

Why Prompt Engineering Matters:

Maximizing Model Potential: Proper prompts can unlock nuanced responses and creative solutions.
Error Reduction: Poorly structured prompts can result in irrelevant or incorrect outputs.
Customizability: Tailored prompts guide models to align with specific tasks or industries.

Best Practices in Prompt Engineering:

Be Specific: Define clear instructions and desired output formats.
Iterate and Experiment: Test multiple variations of prompts to identify what works best.
Leverage Context: Provide additional context to guide the model.

• Example: “You are an expert AI researcher. Explain the difference between supervised and unsupervised learning.”

Use Constraints: Restrict responses to specific formats or styles.

• Example: “Generate a bulleted list summarizing the benefits of LLMs.”

Chain-of-Thought Prompting: Encourage detailed, step-by-step explanations.

• Example: “Walk through the steps a generative AI model takes to create an image, from training on data to generating the final output.”

Conclusion

Generative AI and LLMs are transforming industries, each with unique strengths and applications. While Gen AI caters to a broader range of outputs, LLMs specialize in text-centric tasks. To fully leverage these technologies, understanding their distinctions and mastering prompt engineering is essential.
As AI continues to evolve, the ability to effectively interact with these models will be a valuable skill, unlocking new opportunities in creativity, efficiency, and problem-solving.
Whether you’re an AI developer, business leader, or enthusiast, staying informed and honing your skills is the key to harnessing the power of Gen AI and LLMs in the digital age._
😃

The Future of AI: Building Next-Generation Chatbot Agents with Microsoft Copilot Studio

Emmanuel Adegor — Sat, 04 Jan 2025 01:23:49 +0000

Generative Artificial Intelligence (Gen AI) is rapidly evolving and transforming how we work, collaborate, and innovate. Microsoft's Copilot - built on advanced large language models (LLMs) - has been making waves worldwide. Now, Microsoft Copilot Studio opens the door for developers and organizations to build, customize, and deploy next-generation AI agents tailored to their unique needs.

GEN AI is the future, and the future is here!!

According to Microsoft - An agent is a powerful AI companion that can handle a range of interactions and tasks, from resolving issues requiring complex conversations to autonomously determining the best action to take based on its instructions and context.

In this blog post, we'll explore:

What Copilot Studio is and why it's a game-changer
How to create a Copilot agent that helps employees retrieve policies and procedures within M365
Using multiple SharePoint sites as knowledge sources
Gen Ai Orchestration

What is Microsoft Copilot Studio?

Copilot Studio is a graphical, low-code tool for both creating an agent - including building automation with Power Automate - and extending a Microsoft 365 Copilot with your own enterprise data and scenarios.
One of the standout features of Copilot Studio is its ability to connect to other data sources using either prebuilt or custom plugins. This flexibility enables users to create and orchestrate sophisticated logic, ensuring that their agent experiences are both powerful and intuitive.
The platform's low-code experience puts the power of AI at the user's fingertips, making it accessible even to people without extensive technical backgrounds.
Ref: https://learn.microsoft.com/en-us/microsoft-copilot-studio/fundamentals-what-is-copilot-studio

How to create a Copilot agent

On your web browser, open the Copilot Studio interface within your organization's Microsoft 365 environment.

Click on the"New agent" card,

You could give your Agent a name, choose a base language change icon, a good description, a good set of instructions

Here are my inputs

Name: SharePoint policies and procedures Agent

Description: You are a Policy & Procedure Retrieval Copilot designed to assist employees and managers in finding and understanding policies and procedures. Your responses should be concise and helpful, delivering clear summaries and relevant document links from the SharePoint libraries housing these materials.
Your purpose is to quickly locate and present current, official policies and procedures so that employees and managers can stay compliant, make informed decisions, and follow best practices. Whenever possible, provide brief summaries of key content and highlight critical updates or requirements.
Keep your answers focused and context-aware, and always maintain any confidentiality or compliance guidelines required by the organization. Above all, your mission is to save time and ensure accuracy in retrieving and presenting policies and procedures across multiple SharePoint sites.

Instructions: 
1. Scope of Work
 • Search for and retrieve the most up-to-date policies and procedures documents from the document libraries in the different SharePoint sites.
 • Provide summaries or brief overviews of the key points from each relevant document.

2. Handling of Responses
 • Present information in a concise and clear manner, highlighting critical requirements (e.g., effective dates, compliance steps).
 • Offer direct links or document references so users can easily verify full details in the source.

3. Accuracy and Relevance
 • Prioritize the most recent or officially approved documents.
 • If there are multiple versions, share the link to the latest version.
 • Do not share sensitive or restricted content unless explicitly permitted.

4. Compliance & Confidentiality
 • Always adhere to ABC company data-handling guidelines.
 • Warn users if any content is considered confidential or restricted.

5. Tone & Style
 • Maintain a professional yet approachable tone.
 • If users need deeper explanations or clarifications, promptly request more context.

6. Error Handling
 • If a requested policy cannot be found or is unavailable, provide a polite, clear message and suggest alternative resources or next steps.
 • Encourage users to consult official documentation when in doubt, or escalate to IT

7. Updating Knowledge
 • Periodically confirm that the agent's knowledge sources are still valid.
 • Notify IT Admins if the agent encounters an outdated or missing document.

Using multiple SharePoint sites as knowledge sources

One of the standout features of Copilot Studio is its ability to connect to external data, including SharePoint. This ensures your agent can answer domain-specific questions and retrieve the latest information.

Click on “Add knowledge”

Select “SharePoint”
Input the SharePoint site(s) URL, where the respective “policies and procedures” are located, then

You could edit the “Name” and “Description” fields

Click on “Add”, then click Create

After clicking on “Create”, You should see a progress message that displays “Setting up your agent”

On success, the message “Your agent” is ready will be displayed

Here, you can do further configurations.

Generative Ai Orchestration (preview)

Enable the “Orchestration” field, as it is disabled by default

You could add “Actions” to empower the AI to complete specific tasks for improved engagement or add “Triggers”, for Power Automate workflow integrations, like kicking off an approval or notification workflow for example.

For now, I’ll click “Publish”, and then Test

Here are my test results:

Once I click on the link, it redirects me to the document in the SharePoint library.

For this test, I am using a dummy document, that was renamed “Remote Work & Flexible Scheduling Policy”, hence the message.

Copilot Gen Ai is so powerful that, it can index the documents and also search the document’s content, to know if you’re getting the right document or not.

Such a powerful tool!! 😳

Conclusion

Building next-generation AI agents with Microsoft Copilot Studio is both exciting and transformative. By integrating relevant SharePoint sites and applying sound prompt engineering strategies, you can create a custom Copilot agent that elevates collaboration, productivity, and data-driven decision-making across your organization.
Ready to get started? Head over to your Microsoft 365 environment, fire up Copilot Studio, and start creating your very own next-generation AI agent. By tapping into multiple SharePoint sites and refining your prompt engineering, your organization’s new Copilot will deliver the future of AI right at your fingertips. 🔥

Note:

You may notice that the SharePoint documents used in this demo are not manually tagged with metadata. Copilot AI is powerful enough to index and interpret the content on the fly without strict tagging requirements. However, as your SharePoint libraries grow to hundreds, thousands, or even millions of documents, implementing a consistent metadata strategy can significantly improve discoverability and retrieval performance. Effective tagging ensures that both AI-driven and built-in SharePoint searches return the most relevant results more efficiently as your content scales.