How can we evaluate whether an answer from the LLM is fully supported by the retrieval context? (Consider methods like answer verification against sources or using a secondary model to cross-check facts.)

To evaluate whether an LLM’s answer is fully supported by its retrieval context, developers can use a combination of direct verification against sources and automated cross-checking with secondary models. The goal is to ensure every factual claim or key detail in the answer aligns with the provided context, minimizing unsupported assertions or hallucinations.

First, direct verification against sources involves breaking down the answer into individual claims and checking if each is explicitly present in the retrieval context. For example, if an answer states, “The Treaty of Versailles was signed in 1919,” the context should contain that exact date or a clear implication (e.g., “signed five years after WWI began”). Developers can automate this by using named entity recognition (NER) to extract dates, names, or statistics from both the answer and context, then comparing them. Tools like spaCy or regex patterns can flag mismatches. For nuanced claims, semantic similarity models (e.g., sentence embeddings) can detect paraphrased matches. If the context lacks supporting evidence for any claim, that part of the answer is flagged as unsupported.

Second, using a secondary model to cross-check facts adds another layer of validation. A smaller, specialized model (e.g., a fine-tuned BERT classifier) can be trained to classify whether a statement is supported, contradicted, or not addressed by the context. For instance, if the answer claims “Climate change is solely caused by human activity,” but the context only mentions “human activity contributes significantly,” the secondary model would flag the word “solely” as an overstatement. This approach works well for detecting subtle inconsistencies, such as incorrect causality (e.g., “A causes B” vs. “A is correlated with B”). Developers can integrate this into pipelines using frameworks like HuggingFace Transformers, running the secondary model in parallel with the primary LLM.

Finally, combining both methods improves reliability. For example, a system could first extract answer claims and verify them against the context using exact matches or embeddings. Any ambiguous or unverified claims are then passed to the secondary model for deeper analysis. This hybrid approach is particularly useful for complex answers, such as medical advice or technical documentation, where precision matters. Developers can also log mismatches to refine retrieval systems or adjust the LLM’s prompts. By systematically validating each part of an answer, teams can build trust in LLM outputs while maintaining scalability.