All about RAGs - How to develop RAG based solutions, Origin, Evolution, Use Case

Stimulating Talk, Live Demos and Interactive discussions by Founder Mohammed Lokhandwala : Investor TrustTalk, Mechatron, Chemistcraft ++

All About RAGs: Foundations, Evolution, and Hands-On Application Development

## 👥 Target Audience:

• Beginners to GenAI
• Developers new to RAG-based architecture
• Product/solution architects exploring GenAI integration
• AI/ML enthusiasts interested in applied use cases

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## 🎯 Objectives:

• Understand what RAG is and why it matters
• Trace the origin and evolution of RAG in the context of GenAI
• Learn how to build RAG systems step-by-step
• Explore unique, real-world use cases that RAG uniquely solves
• Position RAG in today’s GenAI ecosystem alongside Agents and other paradigms

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## 🧱 Session Breakdown & Topics:

### 🟦 1. Setting the Context: Why RAG?

• The limitations of LLMs (hallucination, knowledge cutoff)
• Need for real-time, factual grounding
• Transition from static to retrieval-augmented knowledge flows
• Intro to retrieval-augmented generation (RAG)

### 🟦 2. Origins & Evolution of RAG

• Early use of embeddings and vector search (pre-LLMs)
• Evolution of toolchains: Pinecone, Weaviate, LangChain, LlamaIndex

### 🟦 3. Core Components of a RAG Architecture

• Embedding Models (OpenAI, Cohere, SentenceTransformers)
• Vector Databases (Chroma, Pinecone, Weaviate, FAISS)
• Chunking, indexing, retrieval
• Prompt engineering & context injection
• Generation via LLM (OpenAI, Anthropic, Mistral, etc.)
• Evaluation techniques (retrieval precision, groundedness)

### 🟦 4. Building Your First RAG Application: Hands-On Guide

• 🔹 Use case selection (e.g., internal knowledge assistant, customer FAQ bot)
• 🔹 Data preparation: chunking, metadata, format
• 🔹 Creating embeddings (with open-source or API)
• 🔹 Choosing and using a vector DB
• 🔹 Implementing retrieval and generation logic
• 🔹 UI + deployment (e.g., Streamlit, Gradio, LangChain + FastAPI)
• 🔹 Optional: Add memory, conversation history, feedback loops

### 🟦 5. Advanced Topics (Briefly Introduce)

• Hybrid Retrieval (semantic + keyword)
• Structured RAG (e.g., SQL-based retrieval)
• Agent-RAG hybrid systems

### 🟦 6. Use Cases that Only RAG Can Solve Well

• 🔸 Legal document assistants grounded in latest court rulings
• 🔸 Internal enterprise Q&A systems (doc + intranet)
• 🔸 Academic research summarizers across journals
• 🔸 Real-time support chatbots referencing dynamic product manuals
• 🔸 Domain-specific compliance tools

### 🟦 7. RAG vs. Agents vs. Fine-tuning

• When to use RAG vs fine-tuning
• When to layer Agents on top of RAG
• RAG is not obsolete: it's foundational for grounding and precision
• RAG’s role in AI assistants (retrieval for tools, memory, real-time data)
• We can also add "Conceptual introduction to Agentic and tools"

### 🟦 8. Best Practices and Pitfalls

• Chunking strategy & context window size
• Evaluation metrics (factuality, latency, hallucination rate)
• Common mistakes (irrelevant retrieval, poor chunking, bad metadata)
• Cost-performance tradeoffs

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## 🔧 Suggested Tools & Stack for Demos:

• LangChain or LlamaIndex
• OpenAI / Cohere / HuggingFace embeddings
• ChromaDB / FAISS / Pinecone
• GitHub starter kits (can be pre-shared)