Retrieval And RAG Work In Your Product

RAG is a pattern, not a product, and the pattern is mostly ordinary backend engineering: ingest the documents, chunk them sensibly, index them, search, rerank, and render an answer with a citation the reader can click. That is Python, a database and an API, which is our stack. We build it as part of a product we are working on, not as a standalone retrieval consultancy.

RAG (retrieval-augmented generation) is the most common LLM pattern in production today. The pitch is simple: embed your documents, retrieve the relevant ones for each query, stuff them into the prompt, generate. The reality is more complex: most RAG systems retrieve mediocre results, and the LLM gamely answers based on whatever showed up, hallucinating where the retrieval missed.

Chunking is the most-skipped optimisation. Naive 1000-character chunks split sentences and concepts. Better: chunk on semantic boundaries (paragraphs, headings, code blocks) and overlap chunks by 100-200 characters to preserve context. Even better: store the chunk plus parent-document metadata so the LLM has a path to the source.

Hybrid search beats pure semantic search. Vector similarity is great for "find documents about X" but bad for "find documents containing the exact phrase Y". BM25 (keyword search) is the inverse. Combining them — RRF (reciprocal rank fusion) or weighted score — produces dramatically better retrieval than either alone.

Reranking is the highest-leverage RAG improvement. After initial retrieval returns 50 candidates, a cross-encoder reranker (Cohere Rerank, Voyage Rerank, or open-source) scores the top 50 against the query and returns the top 5. The reranker has full attention over both documents and query, unlike embedding similarity which compresses both into vectors. Result: top-5 quality jumps significantly.

AsyncForge has senior engineers shipping production RAG systems. Submit document loaders, chunking strategies, retrieval pipelines, rerankers, evals, or full RAG builds. Light 4 days, Standard 48 hours, Pro 1 day.