Building rag-critic: Factual Grounding & Adversarial Hallucination Auditing in LangGraph

Description and use

<p>When building production Retrieval-Augmented Generation (RAG) pipelines, the primary failure mode isn't retrieval latency or vector index size—<strong>it's silent hallucination</strong>. </p> <p>LLMs frequently generate fluent, highly plausible-sounding claims that are completely unsupported by the retrieved reference chunks. In multi-agent systems and mission-critical workflows, accepting an ungrounded draft can corrupt downstream decision-making.</p> <p>To solve this in cyclic multi-agent graphs, I built and published <a href="https://pypi.org/project/rag-critic/" rel="noopener noreferrer

Problem

<p>When building production Retrieval-Augmented Generation (RAG) pipelines, the primary failure mode isn't retrieval latency or vector index size—<strong>it's silent hallucination</strong>. </p> <p>LLMs frequently generate fluent, highly plausible-sounding claims that are completely unsupported by the retrieved reference chunks. In multi-agent systems and mission-critical workflows, accepting an ungrounded draft can corrupt downstream decision-making.</p> <p>To solve this in cyclic multi-agent graphs, I built and published <a href="https://pypi.org/project/rag-critic/" rel="noopener noreferrer

Outcome

<p>When building production Retrieval-Augmented Generation (RAG) pipelines, the primary failure mode isn't retrieval latency or vector index size—<strong>it's silent hallucination</strong>. </p> <p>LLMs frequently generate fluent, highly plausible-sounding claims that are completely unsupported by the retrieved reference chunks. In multi-agent systems and mission-critical workflows, accepting an ungrounded draft can corrupt downstream decision-making.</p> <p>To solve this in cyclic multi-agent graphs, I built and published <a href="https://pypi.org/project/rag-critic/" rel="noopener noreferrer

Evidence

Building rag-critic: Factual Grounding & Adversarial Hallucination Auditing in LangGraph

Source