Aparna Dhinakaran

This talk explores prompt learning as a method to improve coding agents, contrasting it with traditional Reinforcement Learning (RL). Prompt learning leverages English feedback on agent outputs to iteratively refine system prompts, offering a potentially more efficient and data-light approach for building agents compared to RL's reliance on scalar rewards and extensive data. The core idea is to use LLM-based evaluations to generate actionable feedback that directly informs prompt adjustments.

This talk addresses the challenges of building and iterating on AI agents, particularly in evaluating their performance and identifying bottlenecks. It emphasizes the need for systematic evaluation beyond manual inspection of a few examples. The core thesis is that a self-improving stack for AI agents requires not only improving the agent's prompts and models but also continuously refining the evaluation methods themselves.