Key takeaways
- Why treating LLMs as "guessing machines" matters in high-stakes use cases: a live complaint-classification example shows the same customer statement scored complaint vs. not-complaint depending only on the system prompt, or flipped by a single stray character.
- Why comprehensive validation of a general-purpose LLM is effectively impossible, but a RAG system can be comprehensively validated because it's bounded to a defined document collection and use case.
- The five-step model validation workflow: comprehensive test generation, evaluation with transparent metrics, human calibration, weakness identification, and robustness testing.
- How to build comprehensive test coverage using embeddings, dimensionality reduction, and topic clustering, then stratified or "twinning" sampling, and generate diverse query types (fact extraction, definitional, procedural, multi-hop reasoning, variability/paraphrase, robustness, and numerical/table lookup).
- Why LLM-as-a-judge is unsuitable for regulated industries, and how to use transparent, explainable metrics instead, such as cosine similarity and natural language inference, to measure groundedness, completeness, and answer relevancy.
- How to calibrate machine-generated evaluation scores against human labels using conformal prediction, setting confidence thresholds and flagging uncertain cases for human review.
Overview
In this webinar, an H2O.ai model risk management expert explains why large language models behave like unreliable guessing machines in high-stakes banking and finance use cases, then walks through a five-step framework for validating GenAI systems: test generation, transparent evaluation metrics, human calibration, weakness identification, and robustness testing. He argues LLM-as-a-judge fails regulatory transparency standards, and shows why RAG applications, unlike general-purpose LLMs, can be comprehensively validated since they are bounded to a specific document set.