Advanced RAG
Advanced RAG: multi-strategy retrieval, reranking, refined context and generation, with retrieval strategies, reranking techniques, variants, context enhancement, data flow, evaluation, challenges, optimisation, vector databases, embedding models and best practices.
Cheat sheet · Offered as a template
In this diagram
- Advanced RAG
- Beyond Traditional RAG
- What is Advanced RAG?
- Extends traditional RAG with smarter retrieval, reasoning & adaptation.
- Handles complex queries with better accuracy and relevance.
- Uses multiple techniques to retrieve, rerank, refine and reason.
- Built for scalability, reliability and real-world production.
- Core Components
- User Query
- Advanced Retriever (Multi-Strategy)
- Relevant Docs
- Reranker (Refine Results)
- Refined Context
- Generator (LLM)
- Response
- Advanced Retriever : Uses multiple retrieval strategies.
- Reranker : Reorders results for higher relevance.
- Generator : Generates accurate answer using refined context.
- Advanced RAG Pipeline
- Raw Documents
- • Cleaning • Chunking • Metadata Extraction • OCR / Tables
- Preprocessing
- Embedding Model
- Embedding
- Vector DB + Metadata Store
- Indexing
- Multi-Strategy Retriever (Dense, Sparse, Hybrid, Graph)
- Retrieval
- Top-k Candidate Docs
- Reranker (Cross-Encoder / LLM Rerank)
- Reranking
- Top-n Refined Docs
- LLM (Generate Answer)
- Generation
- Retrieval Strategies (Examples)
- Dense Retrieval
- Vector embeddings similarity search
- Captures semantic meaning
- Sparse Retrieval
- BM25 / TF-IDF based search
- Good for exact term matching
- Hybrid Retrieval
- Combines Dense + Sparse
- Better recall and precision