VQV Signal
AI·rete·RAG uses a pure-Python Rete engine to evaluate YAML rules for consistent, auditable decisions, followed by a retrieval-augmented generation (RAG) step to explain the verdict. This approach separates decision logic from explanation, ensuring repeatable outcomes in sensitive domains.
AI·rete·RAG uses a pure-Python Rete engine to evaluate YAML rules for consistent, auditable decisions, followed by a retrieval-augmented generation (RAG) step to explain the verdict. This approach separates decision logic from explanation, ensuring repeatable outcomes in sensitive domains.
AI-assisted summary based on the listed source.
Hi HN, I built ai·rete·rag because I kept seeing teams put an LLM in charge of decisions that need to be auditable (lending, fraud, clinical triage), then bolt on "guardrails" after the fact. It runs the two in series instead: 1. A pure-Python Rete engine evaluates YAML rules against your facts. The verdict comes...
By placing a rule engine in charge of decisions rather than an LLM, AI·rete·RAG enables auditable and consistent verdicts critical for areas like lending and fraud detection. The RAG component then provides transparent explanations, addressing the need for both reliability and interpretability.
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