Source URL: https://simonwillison.net/2024/Oct/30/llm-as-a-judge/#atom-everything
Source: Simon Willison’s Weblog
Title: Creating a LLM-as-a-Judge that drives business results
Feedly Summary: Creating a LLM-as-a-Judge that drives business results
Hamel Husain’s sequel to Your AI product needs evals. This is packed with hard-won actionable advice.
Hamel warns against using scores on a 1-5 scale, instead promoting an alternative he calls “Critique Shadowing". Find a domain expert (one is better than many, because you want to keep their scores consistent) and have them answer the yes/no question "Did the AI achieve the desired outcome?" – providing a critique explaining their reasoning for each of their answers.
This gives you a reliable score to optimize against, and the critiques mean you can capture nuance and improve the system based on that captured knowledge.
Most importantly, the critique should be detailed enough so that you can use it in a few-shot prompt for a LLM judge. In other words, it should be detailed enough that a new employee could understand it.
Once you’ve gathered this expert data system you can switch to using an LLM-as-a-judge. You can then iterate on the prompt you use for it in order to converge its "opinions" with those of your domain expert.
Hamel concludes:
The real value of this process is looking at your data and doing careful analysis. Even though an AI judge can be a helpful tool, going through this process is what drives results. I would go as far as saying that creating a LLM judge is a nice “hack” I use to trick people into carefully looking at their data!
Via Hacker News
Tags: evals, generative-ai, hamel-husain, ai, llms
AI Summary and Description: Yes
Summary: The text discusses the development of an LLM (Large Language Model) used as a judge in evaluating AI products and their effectiveness. It emphasizes the importance of expert analysis over simplistic scoring methods, suggesting a more nuanced approach to AI evaluation is key to driving business results.
Detailed Description:
The text presents insights from Hamel Husain regarding the evaluation of AI products through the concept of using an LLM as a judge. Here are the key points discussed:
– **Avoiding Simplistic Scoring**: Rather than relying on a 1-5 scoring scale, which may oversimplify the assessment process, Husain advocates for a method he calls “Critique Shadowing.” This approach focuses on qualitative analysis to capture the nuances of AI performance.
– **Expert Involvement**: The recommendation is to work with a domain expert who can consistently deliver evaluations. The expert is asked to answer a binary question (“Did the AI achieve the desired outcome?”) while providing a detailed critique of their reasoning behind the answer.
– **Data Utilization**: The critiques collected from experts serve dual purposes:
– They provide a reliable scoring mechanism to optimize AI outputs against.
– They contain detailed insights that can be used for few-shot prompting, which refines the LLM’s ability to judge effectively.
– **Iterative Improvement**: The process culminates in using the LLM itself as a judge. Continuous iteration on the prompts used for this LLM enables alignment between the machine’s outputs and the expert’s evaluations, fostering improved performance of the AI system.
– **Final Insight**: Husain emphasizes that, while the LLM can act as a useful tool, the real value lies in the careful analysis of data and expert-driven insights. Turning the process of evaluation into a detailed and reflective exercise is portrayed as a strategic advantage in making informed AI decisions.
In summary, this text encapsulates innovative strategies for evaluating AI systems, specifically through the lens of using LLMs, which is pertinent for professionals in the fields of AI evaluation, optimization, and infrastructure development. The emphasis on expert critique facilitates a deeper understanding of LLM outputs and improves AI systems’ alignment with business objectives.