We are worried about hallucinations in our AI-powered customer support tool. Does Spring AI offer any utilities for model evaluation? How can we programmatically check if the response generated by the AI is actually based on the context provided in our RAG pipeline?
3 answers
Spring AI includes a "Model Evaluation" project specifically for this. It provides evaluators like RelevancyEvaluator and FactualityEvaluator. These work by using a "Judge" LLM (usually a more powerful model like GPT-4) to grade the response of your primary model. You pass the original prompt, the retrieved documents (context), and the generated response to the evaluator. It returns a boolean or a score indicating if the answer was grounded in the context. This is essential for building "Guardrails" before showing the output to the end user.
Does running these evaluations significantly increase the total latency and cost, since we are basically calling an LLM twice for every single user query?
I've started using the ChatResponse metadata to at least track token usage and model versions. It’s a good first step toward observability and quality control.
Good point, Brenda. Combining those metrics with the Evaluator API gives you a full picture of both performance and accuracy, which is the gold standard for AI apps.
Michael, you're right; it does add overhead. Most teams don't run evaluations on every single live production request. Instead, they use them during the CI/CD pipeline or on a sampled percentage of traffic to monitor quality over time. You can also use a cheaper model for the "Judge" if the task is simple enough. For critical applications, however, that extra second of latency is often worth the price of ensuring the AI isn't giving out false information.