Aparna Dhinakaran on Muck Rack

Aparna Dhinakaran

Berkeley
As seen in: Forbes, Towards Data Science, Medium, Flipboard, VentureBeat, NewsBreak, BEAMSTART
founder @arizeai. I post about agents and evals. previously: @uber, @ycombinator alum
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Articles

What is an agent harness?

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By Aparna Dhinakaran
| arize.com
A version of this article originally appeared on X. Someone asked me at a hacker event last week: “Can anyone actually tell me what a harness really is?” It was said with real skepticism. The kind of skepticism that says we all use the word “Harness” in the industry, but nobody actually knows what it is. Fair question. Let me try. More of a visual learner?
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What Is Prompt Learning?

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By Aparna Dhinakaran
| Medium
Recently, we started experimenting with a new approach to optimizing LLM prompts that we’re calling “Prompt Learning” (PL). Unlike traditional optimization methods that rely on numerical scores, PL uses natural language feedback to iteratively improve prompts. The roots of this approach are in the Voyager paper by Jim Fan’s team at NVIDIA. It is also alluded to by Andrej Karpathy in several recent tweets, where he argues prompt-centric learning will be a key technique.
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Exploring Prompt Learning: Using English Feedback to Optimize LLM Systems

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By Aparna Dhinakaran
| Towards Data Science
learning (RL) in AI model building has been a growing topic over the past few months. From Deepseek models incorporating RL mechanics into their training processes to other success stories of RL-based improvement, “AI Twitter” has been ablaze. As more agents get deployed, a question emerges: can reinforcement learning control systems be built only in prompts? After all, reinforcement learning is all about using real-world feedback to optimize toward a goal, traditionally by adjusting model weights.
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