Better RAG. Smarter RAG. - Advanced, Agentic RAGs
Details
Topic: Better RAG. Smarter RAG.
Advanced, Agentic & Types of RAG — Demystified
About the speaker
https://www.linkedin.com/in/murari-ramuka-98a440a/
# ✅ 1. RAG (Retrieval-Augmented Generation) – Base Concept
RAG = LLM + external knowledge retrieval
The model retrieves relevant chunks from a knowledge base and uses them to generate accurate, grounded answers.
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# ✅ 2. Advanced RAG – What It Means
Advanced RAG refers to modern improvements over the basic RAG pipeline.
These are techniques, optimizations, and enhancements that make RAG more accurate, scalable, and production-ready.
### 🔹 Key features of Advanced RAG
- Better Retrieval
- Hybrid search (vector + keyword)
- Multi-vector retrieval
- Reranking (cross-encoder)
- Better Chunking
- Semantic chunking
- Recursive chunking
- Dynamic chunking depending on query
- Context Optimization
- Context condensation
- Query rewriting
- Few-shot augmentation
- Advanced Orchestration
- Multi-hop retrieval
- Tool-based retrieval
- Evaluation Frameworks
- RAGAS
- TruLens
- Human + synthetic evals
📌 Think of Advanced RAG as an optimized version of classical RAG.
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# ✅ 3. Agentic RAG – Next Generation of RAG
Agentic RAG introduces agent-like behaviour, where the LLM doesn’t just retrieve and answer—it reasons, plans, decides, and iterates.
### 🔹 Key properties of Agentic RAG
- Planning
- Breaks the user query into steps
- Executes multi-step reasoning for retrieval
- Self-reflection
- Evaluates its own retrieved results
- Refines instructions and retries
- Tool Use
- Calls tools (database queries, APIs, calculators)
- Autonomous Actions
- Runs loops: retrieve → evaluate → improve → answer
- Multi-Agent Systems
- One agent for retrieval
- One for analysis
- One for verification
📌 Agentic RAG = RAG + Autonomous Cognitive Loop + Tool Use
The model becomes a problem-solver, not just a retriever + generator.
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