rag-engineer

Build a retrieval-augmented generation pipeline by orchestrating document chunking, vector search, and context injection to ground LLM responses in retrieved facts, reducing hallucinations. You configure semantic chunking and a retrieval pipeline to inject relevant context into generation workflows.

Implement semantic chunking by preprocessing documents into segments that preserve meaning and context before generating vector embeddings. This approach maintains semantic integrity during retrieval pipeline design, ensuring the retrieved chunks provide coherent context to the LLM.

Vector search and hybrid retrieval improve context injection by combining coarse-to-fine retrieval stages and reranking to maximize relevant context. This multi-stage approach optimizes the context window, ensuring the LLM receives the most accurate and pertinent information for generation.

Yes, you need a separate embedding model and a vector store to set up a RAG workflow. The pipeline requires integration with these external components to generate vector embeddings and perform similarity search before context injection into the LLM.

Yes, you can combine multiple retrieval stages and reranking in a single RAG pipeline. The workflow supports retrieval pipeline design that integrates coarse-to-fine stages and reranking, enabling flexible semantic and hybrid search querying across your document collection.

Your retrieval-augmented generation workflow returns irrelevant context when semantic chunking fails to preserve meaning or the vector search lacks reranking. Optimize your retrieval pipeline design by combining coarse-to-fine stages and hybrid search to maximize relevant context window utilization.