How to detect hallucinations when evaluating your LLM.
This is a fantastic article (link in the next tweet) with step-by-step instructions on how to use an LLM-as-a-judge approach to detect hallucinations.
Here is a quick summary:
1. You'll use an LLM to judge outputs
2. You'll use a prompt (see image) that will generate a metric
3. You'll use structured outputs (for example, JSON)
4. You'll build an inference pipeline
5. You'll set up your evaluation process
At a high level, the idea is to produce your outputs and run them through the judge to determine whether there are hallucinations or not.
The key here is to do this quickly (keeping latency to a minimum), reliably (we need to trust the evaluation results), and as simply as possible (we need a way to orchestrate all of this.)
The article uses Opik (an open-source evaluation library) to orchestrate this process. There's a Colab with all the code you need.