Evaluation is one of the most critical steps in bringing an LLM into our routine workflow. Without proper evaluation, it is difficult to know whether the model’s output truly meets our expectations or simply appears convincing on the surface. By systematically evaluating the results, we can measure the quality of the responses, identify weaknesses in the prompts or system design, and iteratively improve the overall performance of the system.

For example, you can evaluate a customer support system by preparing a dataset of common support questions and comparing the model’s responses with the expected answers. Questions like refund policies, order tracking, or troubleshooting instructions can be used as benchmarks. By comparing the AI’s response with human-written answers, you can measure accuracy, helpfulness, and whether the response follows company policy.

You start the workflow with the initial prompts and feed in the data. The LLM generates the output in the spreadsheet. You then label which dimensions you want to improve, go back to iterate on the prompt, and generate new results. Over time, this process helps you gradually refine the prompt and improve the quality of the model’s responses.