"What 22 Days of Building AI Systems Taught Me: Grounding, Evals, and Control"

Description and use

<h1> What 22 Days of Building AI Systems Taught Me: Grounding, Evals, and Control </h1> <p>I started this learning sprint with a familiar but incomplete idea: an LLM was a powerful chatbot, and the hard part of using one was writing a clever prompt.</p> <p>Twenty-two days later, I have a different model. An LLM is a probabilistic component inside a larger software system. A useful AI feature needs the same things other production software needs: clear inputs, constrained behavior, observability, and a way to tell whether a change made it better or worse.</p> <p>This is the path I took: fundame

Problem

<h1> What 22 Days of Building AI Systems Taught Me: Grounding, Evals, and Control </h1> <p>I started this learning sprint with a familiar but incomplete idea: an LLM was a powerful chatbot, and the hard part of using one was writing a clever prompt.</p> <p>Twenty-two days later, I have a different model. An LLM is a probabilistic component inside a larger software system. A useful AI feature needs the same things other production software needs: clear inputs, constrained behavior, observability, and a way to tell whether a change made it better or worse.</p> <p>This is the path I took: fundame

Outcome

<h1> What 22 Days of Building AI Systems Taught Me: Grounding, Evals, and Control </h1> <p>I started this learning sprint with a familiar but incomplete idea: an LLM was a powerful chatbot, and the hard part of using one was writing a clever prompt.</p> <p>Twenty-two days later, I have a different model. An LLM is a probabilistic component inside a larger software system. A useful AI feature needs the same things other production software needs: clear inputs, constrained behavior, observability, and a way to tell whether a change made it better or worse.</p> <p>This is the path I took: fundame

Evidence

"What 22 Days of Building AI Systems Taught Me: Grounding, Evals, and Control"

Source