Supercharge Your AI Agents with Evaluations
Editor’s note: Aditya Palnitkar is a speaker for ODSC East this May 13th-15th! Be sure to check out his talk, “Evals for Supercharging your AI Agents,” there to learn more about AI agent evaluations!
Evaluations are a frequently overlooked aspect of developing LLM applications and AI agents. Which is unfortunate, given how profoundly they can impact your development process.
A good evaluation system can help you:
- Catch Regressions and make sure you’re prioritizing the ‘do no harm’ principle and not causing regressions with your flashy launch
- Report and set goals using metrics that correlate with your user’s experience
- Create your Roadmap by helping you identify areas for improvement.
The blog delves into practical strategies for tackling these challenges, from using scalable LLM judges to leveraging conversation-level metrics for deeper insights.
There are two steps to building a great evaluation system. The first one being:
Step 1: BYOM: Build Your Own Metric
Unlike traditional machine learning tasks where success can often be measured by a single metric, evaluating AI agents that interact with humans is far more complex. There’s no clear-cut “ground truth” when it comes to human-AI interactions. This is where custom metrics come into play.
Imagine an AI realtor. Its success isn’t just about providing accurate property information; it also needs to avoid biased responses and maintain a professional tone. On the other hand, a medical AI assistant must prioritize minimizing hallucinations to ensure patient safety.
Step 2: BYOD: Build Your Own Dataset
Once you have your metric, the next step is to build your dataset and labeling pipeline. Given a user’s query, and your AI agent’s response, how do you determine if the response was good or bad based on your metric definition?
As always, it depends.
Want to kickstart your labeling process and create a pristine dataset that will guide your evaluation efforts for months to come? Get help from experts to label your dataset.
Need to get a reliable read on your metrics to report to your stakeholders? Set up a human labeling operation with rigorous monitoring, training and evaluation for the labelers.
Now that your team is growing and you are pushing tens of diffs into production daily, do you need a scalable method to make sure that none of your diffs regresses your key metrics or user experience? Enter LLM judges, which need a delicate dance to ensure reliability and purging of all inherent biases.
And this is just the first choice you need to make amongst many- next you need to choose whether to focus on a fixed or a live dataset, and whether to evaluate at a single turn or an entire conversation at a time.
Why This Matters
This sounds like a lot of work, and it is! But once you set up your evaluation pipeline by making decisions along these axes, you can supercharge your AI agent development process by putting together incredibly powerful feedback loops that guide you and make sure you are making progress.
More About My ODSC East Session on AI Agent Evaluations
Join me as I talk about how to put a world-class AI agent evaluation system to work toward your LLM application. I’ll talk about the intricacies and pitfalls involved in many parts of this process:
Generating human-labeled high-quality eval dataset, scaling this by using LLMs as a judge, using LLMs to simulate users for testing multi-turn scenarios for LLMs, creating benchmark datasets, creating eval systems for catching hallucinations through LLM judges, selecting metrics that closely align with your business goals, and monitoring failure modes in your AI agent through specialized datasets.
About the Author
Aditya is a staff engineer / TL at Meta. After his undergrad from BITS Pilani, he graduated with an MS in CS from Stanford, with a focus on using ML for performing graph analysis and predictions. In his decade of working in AI / ML, he has gained unique experience and insights working on state-of-the-art recommendation models backing Facebook Reels and Facebook Watch, which serve 1B+ users every day. He is now tech-leading a team working on evaluating AI agents being built at Meta.
