Sharad
Sharad
AI

Testing Retrieval Before Tuning Prompts

A support notebook returned convincing answers from outdated installation notes. The first explanation sounded plausible, but it did not account for what happened during an ordinary working day. I wanted to understand the situation well enough to make a modest change and recognize whether it helped.

Start with an observable example

I compared retrieved passages against ten questions from the support team before changing the prompt. I kept the investigation narrow so that each observation could be checked against a concrete example. Where the evidence was incomplete, I recorded the open question instead of quietly treating an assumption as a requirement.

Version labels and a small relevance review made the results easier to trust.

Make the next step measurable

The next iteration is deliberately small. I will repeat the original scenario, compare the result with the earlier behavior, and ask someone unfamiliar with the change to explain what they see. That review should reveal confusing details that are easy to miss when I already know the intended outcome.

I also want to keep the reasoning alongside the work. A short note about the original constraint, the evidence behind the change, and the remaining uncertainty will help when the situation changes again. The goal is a practice that can be understood and improved without reconstructing the entire investigation.

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