🛑 RAG retrieves what's similar. Not what's right.
That's the core problem.
• You ask a question.
• RAG searches a vector database,
• Finds the closest embeddings, and
• Returns what looks relevant.
Similar is not correct.
It's an approximation to what may look right.
In a chatbot, close enough works. In a regulated decision, close enough is a compliance failure.
When an agent needs to resolve a policy, apply a regulation, or determine what rule governs this specific situation…
…The similarity is not the job. Resolution is.
✨ Themis knowledge engine doesn't search for the nearest match.
It retrieves exactly what applies and infers what's relevant to solve the problem.
💡Deterministic. Repeatable. Explainable.
RAG says “here are the top 5 chunks that look related.”
Themis knowledge engine says “this is what applies and here is why.”
One guesses. The other resolves.
LLMs give your agents creativity. The right tools give them correctness.
Agentic AI for regulated industries. AI agents you can trust.
💣 With Themis agents born governed.
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Posted here.
Published July 14th, 2026 at 07:30 am
