LLM Hallucination Detection and Mitigation

Hallucination - where an LLM generates plausible-sounding but factually incorrect content - is the primary reliability risk in production AI systems. For a business like ShopMax India, a model confidently stating wrong product prices or return policies can directly harm customer trust. Detecting and mitigating hallucinations is essential before deploying any LLM to customers.

The most effective mitigation strategies are grounding (providing factual context via RAG), self-consistency checking (running the same prompt multiple times and comparing answers), and confidence scoring (checking whether the model assigns low probability to its output). Detection can be automated using NLI (natural language inference) models to check whether generated text is entailed by the source context.

The below example shows a hallucination detection pipeline that uses an NLI model to verify whether a generated product description is grounded in the provided source facts.

It gives the following output,

Hallucination Detection Results:
--------------------------------------------------
[GROUNDED] The Sony WH-1000XM5 costs Rs 29999 with 30 hours bat
  Entailment score: 0.891
[HALLUCINATION] The Sony WH-1000XM5 has a 2-year warranty and c
  Entailment score: 0.124
[GROUNDED] Sony headphones are available in Mumbai and Bangalore
  Entailment score: 0.812

The NLI model correctly flags the second text as a hallucination - the warranty is wrong (1 year not 2) and the price is wrong. In production, run this check on every LLM response before serving it to ShopMax customers, automatically falling back to a templated response when the entailment score drops below your threshold. Pair this with RAG to ensure the model always has accurate product data in context.