LLM based evaluation pipeline to reduce human effort on answers validation

LLM-based evaluation pipeline to reduce human effort on answer validation

The Challenge

Manually validating answers after updates in the application was time-consuming, repetitive, and required significant human effort. This bottleneck made rapid iteration and quality control difficult, blocking efforts to reduce human effort in QA validation.

Hypothesis

Leveraging an LLM to evaluate answer quality by comparing old and new responses could reduce the burden on human validators, especially when the model’s answers are consistent and clearly grounded in the provided context. Positioning this as an LLM evaluation pipeline for automated AI answer validation with built-in AI hallucination detection would further improve reliability.

Execution

Implemented an LLM evaluation pipeline that:

  • Automatically compares new answers to existing ones for consistency.
  • Flags significant changes or mismatches for human review.
  • Performs a hallucination check to determine if the answer is grounded in the retrieved context, leveraging AI hallucination detection.

Outcomes

Human validation time was significantly reduced (85–90% of the human effort was reduced), enabling faster deployment cycles and improving overall efficiency in QA evaluation without compromising answer quality demonstrating a clear reduce human effort in QA validation impact.

Project Highlights

85–90%

of the human effort was reduced