LLM factuality checking system design 4 questions in 3 chapters

  1. 01Designing the checker
  2. 02Measuring and scaling the checker
  3. 03Sources

Designing the checker

From one holistic judge call to specialized judges, confidence gating and targeted rechecks.

  • 1.1 How would you design an automated factuality checker for generated text at scale? Hard 4 min

    I start from a gold set labelled by expert annotators with verdicts and rationales, then build a judge pipeline. Specialized judges check literal truth and misleading implications separately. Confident verdicts stop there; uncertain ones go to a router that turns the judges' doubts into atomic claims and rechecks each against the source or the web. Any-fail aggregation and overturn rules give the final verdict.

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  • 1.2 How do claim decomposition and atomic fact verification work? Hard 2 min

    Instead of one verdict for a whole text, I break it into short self-contained claims and check each against a trusted source, so one wrong detail is not hidden by many correct ones. FActScore scores the share of supported atomic facts; SAFE checks each fact with search; NLI models test whether the source entails a sentence. Decomposition still misses misleading-but-true framing, so I keep a holistic implication check.

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Measuring and scaling the checker

Metrics for a judge, rationale alignment, and the cost and latency trade-offs of cascades.

  • 2.1 How do you evaluate an LLM judge for factuality? Hard 3 min

    I compare the judge with a human-labelled gold set and report precision and recall for each class, not one accuracy number. For a gate that must stop bad content, recall on failing items is the primary metric, with precision on passing items close behind. I also check that the judge fails items for the same reasons people do, and I track cost, calls per item and latency.

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  • 2.2 How do you cut the cost of LLM judging at scale without losing recall? Hard 2 min

    I spend expensive calls only where they change the answer. Easy items get one cheap pass and stop when the judge is confident; uncertain items escalate to more or stronger checks, which is an LLM cascade. I verify that the confidence signal is calibrated on the gold set, measure cost, calls per item and median latency per tier, and accept slower hard cases if throughput allows.

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Sources

Source: Netflix TechBlog: Fact-Checking at Scale: Building a More Accurate and Efficient Synopsis Factuality System Source: Zheng et al., Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena Source: Wang et al., Self-Consistency Improves Chain of Thought Reasoning Source: Min et al., FActScore Source: Wei et al., Long-form factuality in large language models (SAFE) Source: Laban et al., SummaC Source: Manakul et al., SelfCheckGPT Source: Liu et al., G-Eval Source: Chen et al., FrugalGPT Source: Tian et al., Just Ask for Calibration Source: Xiong et al., Can LLMs Express Their Uncertainty? Source: scikit-learn docs: precision, recall and F-measures