Gen AI outputs need to be evaluated for accuracy, relevancy, comprehensiveness, how they de-risk bias and toxicity, and more. This should be done before they are deployed to production and acted on, to avoid issues like performance, ethical matters, legal issues and disruptions.
The methods that can be used to evaluate outputs include:
- Comparing the results to the data source they were retrieved from
- Ensuring consistent responses by running similar prompts multiple times
- Using LLM-as-a-Judge to allow an additional LLM to evaluate the results
- Testing outputs and fine-tuning the model for adherence to industry-specific knowledge requirements or specific brand voice
- Reviewing responses against a checklist of essential components for the given topic or field
- Implementing guardrails and filters for toxicity, hallucinations, harmful content, bias, etc.
- Implementing guardrails for security and privacy
- Continuous monitoring and feedback loops to ensure ongoing quality and relevancy
- Establishing LLM metrics to track the overall success of the AI model in meeting its intended purpose
