rag-implementation

Build a RAG pipeline by wiring vector databases, embedding models, and retrievers to fetch context for LLMs. This Skill orchestrates the end-to-end flow, applying chunking, metadata, and reranking strategies to provide grounded answers with citations from local or external knowledge sources.

Improve retrieval-augmented generation relevance by applying hybrid search, multi-query retrieval, and contextual compression. These modular patterns balance performance and accuracy by refining the chunks passed to the LLM, ensuring the generated answers are highly targeted to your query.

Yes, you can use contextual compression and multi-query retrieval for knowledge-grounded AI. These techniques extract the most relevant information from fetched documents, allowing your chatbots and assistants to maintain provenance and cite sources accurately.

Yes, a vector database and embedding models are required to set up semantic search. This Skill integrates these components to store and retrieve document embeddings, applying rerankers and effective chunking to ensure accurate semantic matches across your knowledge base.

Parent document retrieval works by chunking documents into smaller segments for precise embedding matches, then fetching the larger parent document to provide full context to the LLM. This balances retrieval accuracy with providing comprehensive background information for grounded answers.

Avoid hybrid search in RAG pipelines when your knowledge base is small or highly uniform, as the overhead of combining keyword and semantic search may not improve accuracy. Relying on standard semantic search with proper metadata might be sufficient for simpler document QA tasks.