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


Details
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
## ๐ฅ 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
***
## ๐ฏ 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 todayโs GenAI ecosystem alongside Agents and other paradigms
***
## ๐งฑ Session Breakdown & Topics:
### ๐ฆ 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)
### ๐ฆ 2. Origins & Evolution of RAG
- Early use of embeddings and vector search (pre-LLMs)
- Evolution of toolchains: Pinecone, Weaviate, LangChain, LlamaIndex
### ๐ฆ 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)
### ๐ฆ 4. Building Your First RAG Application: Hands-On Guide
- ๐น Use case selection (e.g., internal knowledge assistant, customer FAQ bot)
- ๐น Data preparation: chunking, metadata, format
- ๐น Creating embeddings (with open-source or API)
- ๐น Choosing and using a vector DB
- ๐น Implementing retrieval and generation logic
- ๐น UI + deployment (e.g., Streamlit, Gradio, LangChain + FastAPI)
- ๐น Optional: Add memory, conversation history, feedback loops
### ๐ฆ 5. Advanced Topics (Briefly Introduce)
- Hybrid Retrieval (semantic + keyword)
- Structured RAG (e.g., SQL-based retrieval)
- Agent-RAG hybrid systems
### ๐ฆ 6. Use Cases that Only RAG Can Solve Well
- ๐ธ Legal document assistants grounded in latest court rulings
- ๐ธ Internal enterprise Q&A systems (doc + intranet)
- ๐ธ Academic research summarizers across journals
- ๐ธ Real-time support chatbots referencing dynamic product manuals
- ๐ธ Domain-specific compliance tools
### ๐ฆ 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
- RAGโs role in AI assistants (retrieval for tools, memory, real-time data)
- We can also add "Conceptual introduction to Agentic and tools"
### ๐ฆ 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
***
## ๐ง Suggested Tools & Stack for Demos:
- LangChain or LlamaIndex
- OpenAI / Cohere / HuggingFace embeddings
- ChromaDB / FAISS / Pinecone
- GitHub starter kits (can be pre-shared)

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