Hi everyone,
When building production RAG pipelines with Weaviate in regulated domains (finance, legal, compliance), two common failure modes occur:
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LLMs hallucinating facts or misinterpreting retrieved context chunks.
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Relying on secondary LLM-as-a-judge calls that add latency and cost just to check faithfulness.
I built two open-source verification libraries to address this:
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groundkit(GitHub - ss1738/groundkit: Deterministic grounding of LLM output against authoritative sources. Zero dependencies. · GitHub): Enforces token-to-source attribution against Weaviate’s retrieved chunks to guarantee line-by-line evidence without running recursive LLM judge calls. -
certain(GitHub - ss1738/certain: Conformal prediction for Python: calibrated prediction sets and principled abstention. Zero dependencies. · GitHub): Implements distribution-free conformal prediction bounds ($\alpha \le 0.01$) to mathematically route ambiguous retrieval/synthesis outputs to human review.
Would love feedback from anyone building enterprise RAG on Weaviate: how are you handling statistical confidence bounds and citation verification in production?