rag-engineer

To optimize RAG retrieval pipelines, you architect systems connecting raw documents to LLM context via precise embedding selection, semantic chunking, and hybrid search strategies, ensuring output quality reflects input relevance and reducing hallucinations.

Semantic chunking splits documents by meaning using parent-child relationships, overlap, and metadata enrichment to preserve context. This hierarchical retrieval approach maintains the surrounding meaning of text segments, keeping retrieval precise for diverse query intents.

You need hybrid search for your RAG pipeline when handling diverse query intents that require combining semantic and keyword techniques. This approach reranks results and measures retrieval quality separately from generation to avoid hallucinations in production LLM applications.

Evaluating retrieval quality separately from generation in RAG systems involves measuring how well vector search and reranking return relevant context before the LLM generates text. This isolation prevents retrieval errors from causing generation hallucinations.

Yes, building a RAG pipeline requires understanding embedding models and vector databases to select options matching your content types. This knowledge keeps retrieval precise and scalable across production LLM applications.

The best way to chunk manuals semantically for a support AI is to build a pipeline that splits documents by meaning, refreshes embeddings, and applies hybrid retrieval. This preserves context and handles diverse query intents accurately.