AI Prompt for RAG Pipelines
Production RAG recipe: token-based sliding window chunking, voyage-3-large embeddings, MongoDB Atlas Vector Search storage, Cohere Rerank 3.5 reranking. Includes retrieval evals.
More prompts for RAG Pipelines.
Implement query decomposition to improve retrieval recall for support tickets using jina-embeddings-v3 + multi-vector (per chunk).
Production RAG recipe: recursive character chunking, mxbai-embed-large embeddings, Redis Vector storage, Voyage rerank-2 reranking. Includes retrieval evals.
Production RAG recipe: semantic (embedding-based) chunking, stella_en_1.5B_v5 embeddings, Chroma storage, mxbai-rerank-large reranking. Includes retrieval evals.
Hybrid BM25 + dense retrieval architecture with Cohere Rerank 3.5 cross-encoder reranking, tuned for customer interview transcripts.
Production RAG recipe: token-based sliding window chunking, stella_en_1.5B_v5 embeddings, Weaviate storage, mxbai-rerank-large reranking. Includes retrieval evals.
Production RAG recipe: token-based sliding window chunking, cohere-embed-multilingual-v3 embeddings, pgvector storage, Cohere Rerank 3.5 reranking. Includes retrieval evals.
Replace the bracketed placeholders with your own context before running the prompt:
[{chunk_id, quote, doc_url}]— fill in your specific {chunk_id, quote, doc_url}.[doc_id]— fill in your specific doc_id.