Aditya Palnitkar will be speaking at the ODSC East conference from May 13th to 15th, focusing on the crucial aspect of evaluating AI agents in his talk titled “Evals for Supercharging your AI Agents.” This often-underestimated process is vital for creating effective LLM applications. A well-structured evaluation system can help catch regressions, set user-experience-aligned goals, and identify improvement areas. Aditya will discuss practical strategies, including building custom metrics and datasets, and leveraging LLMs for human-like evaluations. Attendees will gain insights into creating feedback loops that enhance AI agent development efficiency and reliability. Join him to learn how to optimize your AI evaluations and advance your projects.
Editor’s note: Aditya Palnitkar, a prominent figure in AI, will be speaking at ODSC East from May 13th to 15th. Don’t miss his session titled “Evals for Supercharging Your AI Agents,” where he will delve into the crucial topic of evaluating AI agents.
Evaluating AI agents might not seem exciting, but it is a vital step in developing effective LLM applications. A well-structured evaluation system can significantly enhance the development process and improve user experience. Here are some key benefits:
– Catch regressions to ensure that updates do not negatively impact the user experience.
– Set and report goals using metrics that align with users’ experiences.
– Identify areas for improvement, creating a clear roadmap for development.
To build a robust evaluation system, two essential steps are required:
Step 1: Build Your Own Metric (BYOM)
Unlike traditional machine learning tasks where success can be measured by a single metric, evaluating human-AI interactions is more complex. Custom metrics are necessary to assess performance accurately. For example, an AI realtor needs to provide not only accurate listings but also maintain professionalism, while a medical assistant must prioritize patient safety by minimizing inaccuracies.
Step 2: Build Your Own Dataset (BYOD)
After defining your metrics, creating a suitable dataset is crucial. Assessing whether an AI response meets your criteria requires a well-structured labeling pipeline. Consider engaging experts to ensure high-quality labeling of your dataset to enhance evaluation efficiency.
While setting up this evaluation pipeline demands effort, it ultimately leads to more effective AI agent development, helping create powerful feedback loops to guide your progress.
In his session at ODSC East, Aditya will share insights on generating human-labeled datasets, using LLMs for testing, and selecting metrics aligned with business goals.
About Aditya: As a staff engineer at Meta, Aditya Palnitkar has a rich background in AI and machine learning. After completing his degrees from BITS Pilani and Stanford, he has spent a decade honing his expertise, particularly in recommendation systems used by millions every day.
This upcoming talk promises to be an invaluable experience for anyone interested in enhancing the effectiveness of AI agents.
Tags: AI, LLM Applications, AI Agent Evaluations, ODSC East, Custom Metrics, Data Science Conference
What are AI agents and how do they work?
AI agents are software programs that can perform tasks and make decisions using artificial intelligence. They analyze data, learn over time, and aim to assist users in various activities, like answering questions or controlling smart devices.
Why is evaluation important for AI agents?
Evaluation helps us understand how well an AI agent is performing. By measuring their success, we can identify areas for improvement, ensuring they become more effective over time in doing their jobs.
How can I supercharge my AI agents with evaluations?
You can enhance your AI agents by regularly assessing their performance. Use metrics like accuracy, speed, and user satisfaction to track progress. Implement feedback loops to constantly refine their capabilities based on evaluations.
What tools are available for evaluating AI agents?
There are several tools available for evaluating AI agents, including data analytics software, performance tracking tools, and user feedback surveys. These tools help you see how well your agents are doing and provide insights for improvement.
Can I use evaluations to train AI agents?
Yes, evaluations play a crucial role in training AI agents. By analyzing their performance, you can identify what needs to be improved and provide targeted training. This way, the agents learn from their mistakes and become more efficient.
