Hallucinations remain a persistent hurdle for anyone building with LLMs—even in systems that leverage retrieval augmented generation (RAG).

A new open-source package called DeepEval makes evaluation and hallucination mitigation much easier. Here are a few practical techniques using DeepEval:

(1) Pinpointing contradictions by comparing outputs against known facts or provided context. For example, if your context says “The Great Wall of China was built primarily with stone and brick,” but the output claims “It’s made entirely of gold,” DeepEval’s HallucinationMetric can automatically flag that contradiction.

(2) Utilizing the G-Eval framework to assess LLM outputs using custom criteria enabled by chain-of-thought evaluation techniques. For instance, you can define multi-step criteria to check correctness (e.g., verifying that Apollo 11 landed on the moon on July 20, 1969) and let G-Eval highlight any mismatch, even if it’s just a single date off.

(3) RAG-specific metrics – measuring faithfulness, precision, and recall to ensure retrieved information aligns with final outputs.

As language models become increasingly integrated into business workflows, ensuring factual correctness is crucial. These detection strategies can help teams proactively address hallucinations and produce more reliable answers—even when the LLM attempts to fill gaps with its own imagination.

GitHub repo github.com/confident-ai…