Chain of Thought – A Query Transformation Technique for Advanced RAG
Less Abstract Approach

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Series
This series unpacks the Query Optimization Techniques in Advanced Retrieval-Augmented Generation (RAG). Learn the theory, practical examples, code samples, and visual breakdowns of Techniques .
Less Abstract Approach

RAG Fusion Approach

HyDE generates a hypothetical document ( based on Pre-Trained Knowledge of LLM ), from User Query, then instead of creating vector embeddings of user query and search in our vector database, it creates embeddings of that hypothetical Document , and f...

In Retrieval-Augmented Generation, success depends on more than just good data — it’s about choosing the smartest routes for storage, search, and retrieval. Every step, from indexing information to delivering answers, can be optimized. Query Routing ...

Fan Out Approach

Advanced RAG : Rag with optimization , to make Response more Accurate for User . RAG is Retrieval-Augmented Generation , its basically Retrieving (Fetching) Relevant information from some External data source and providing it as a context to LLMs, Au...
