Unlocking Business Knowledge Through RAG Based AI Development

Описание: 🔴 Unlocking Business Knowledge Through RAG-Based AI Development
(How Retrieval Augmented Generation Brings Your Enterprise Data into AI)
📅 February 5 · 9 PM IST – Set Reminder ▶️

Generative AI becomes dramatically more powerful when it can use your business data instead of relying only on what a model already knows. Retrieval Augmented Generation (RAG) is the framework that makes this possible—grounding prompts with relevant, real-time information from documents, databases, and enterprise knowledge sources.

This session provides a practical introduction to RAG and how Microsoft Foundry enables developers to build RAG-powered AI solutions that are accurate, trustworthy, and enterprise-ready.

Led by Ranjith Kumar, Corporate Trainer, the session demonstrates how to add data, create indexes, and integrate them with generative models to unlock meaningful business insights.

🔍 What you’ll learn:

1. What Retrieval Augmented Generation (RAG) Is
Why RAG is essential for enterprise AI applications.
How RAG enables AI to respond with grounded, up-to-date information.
The core workflow: retrieve → augment → generate.

2. Why RAG Matters for Business AI
Improves accuracy by preventing hallucinations.
Allows AI to use proprietary knowledge securely.
Makes AI suitable for workflows involving policies, documents, FAQs, customer data, and expertise retrieval.

3. RAG in Microsoft Foundry
How Microsoft Foundry supports RAG development end-to-end.
Uploading and managing enterprise data sources.
Creating indexes for fast vector-based retrieval.
Integrating indexes with LLMs using Foundry’s orchestration tools.

4. Adding Data & Creating Indexes
Supported data types and ingestion options.
How indexing works—embeddings, chunking, metadata tagging.
Best practices for organizing and preparing data for high-quality retrieval.

5. Building RAG-Based AI Solutions
Connecting retrieval steps with generative models.
Designing prompt flows that incorporate retrieved context.
Common patterns for search-augmented chatbots, document Q&A, and knowledge assistants.

6. Real-World Use Cases
Policy and compliance assistants
Customer support knowledge bots
Internal document search and Q&A
Research and summarization automation
Business decision support using contextual insights

7. Best Practices for High-Quality RAG Systems
Chunk sizing, relevancy tuning, prompt design, and evaluation.
Handling confidential data securely.
Improving retrieval precision and reducing noise.

8. Live Q&A and Applied Guidance
Ask questions about implementation, indexing, architecture, and optimization.
Hands-on insights for developers and enterprise AI teams.

🎯 Who should attend?
Developers & AI engineers
Data and application architects
Teams building chatbots, Q&A, or internal knowledge tools
Professionals integrating enterprise data with generative AI
Anyone exploring RAG as a foundation for trustworthy AI

Speaker:
Ranjith Kumar
Corporate Trainer | Koenig Solutions Pvt. Ltd.

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🧠 If you want to make AI intelligent with your business knowledge, this session will show you exactly how RAG unlocks next-level accuracy and enterprise value.
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