AI Engineering
Beyond the Basics: How to Drastically Increase the Precision of Retrieval-Augmented Generation
A simple RAG implementation with vector embeddings often fails in practice. If the user asks vague questions or searches for specific product codes, the vector search lacks the exact context. Advanced RAG architectures combine semantic searches with traditional keyword search, query rewriting, and cross-encoder re-ranking to achieve maximum precision.
Hybrid Search: Combining Vector Embeddings and BM25
Combining semantic meaning with exact keyword matching for unbeatable findability.
Query Rewriting and Expansion
Having a smaller model reformulate user questions into optimal search queries.
Cross-Encoder Re-ranking with Cohere
Reordering the top 50 found documents based on deep contextual relevance.
Hierarchical Chunking and Parent-Child Retrievals
Index smaller text chunks for accurate matching, but send the larger parent context to the LLM.
Conclusion and Best Practices
Advanced RAG techniques transform a weak AI search function into a robust, factual enterprise knowledge source.
Next: Multimodal AI Models: Processing Visual and Auditory Data in LLM Pipelines
