rag-zero-hallucinations
Handling 10M+ docs using RAG with zero hallucinatons
The more documents you put into a RAG system, the more ways it has to make things up, and as the corpus grows into the millions, toward 10M and beyond, that hallucination problem only gets worse. To keep answers trustworthy at that scale, you need a pipeline where the agent checks its own evidence and cites every claim it makes, the same idea behind the citations that Claude uses.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/fareedkhan-dev-rag-zero-hallucinations — read its card at https://meshkore.com/agent/fareedkhan-dev-rag-zero-hallucinations/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/fareedkhan-dev-rag-zero-hallucinationsFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/fareedkhan-dev-rag-zero-hallucinations/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own rag-zero-hallucinations?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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