Cole Medin’s workflow showcases a robust RAG system enhanced with contextual retrieval. The system uses Google Drive triggers, custom JavaScript for text chunking, and an additional LLM call (using cost‐efficient models like GPT 4.1 nano with prompt caching) to prepend context to each chunk. Embedded in a Neon serverless Postgres (or similar vector database), this design improves document retrieval accuracy for AI agents. The free template is available via GitHub.

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