DSPy Retrieval Loops
Structured DSPy-style experiments for improving retrieval and answer quality through optimization, evaluation-aware prompting, and tighter system feedback loops.
DSPy Retrieval Loops is an experiment in making retrieval systems more systematic. Instead of treating prompts and retrieval settings as static configuration, I used DSPy-style workflows to test whether answer quality could be improved through tighter optimization loops tied to evaluation.
The interesting part was not simply prompt tuning. It was the interaction between retrieval behavior and downstream reasoning. A system can fail because it retrieves the wrong evidence, because it frames the right evidence poorly, or because the optimization target is not aligned with actual usefulness. DSPy makes those dependencies easier to expose.
This project focused on building a cleaner loop between retrieval, prompting, and evaluation. When the system improved, I wanted to know why. When it failed, I wanted to know whether the issue was in retrieval quality, answer construction, or the objective being optimized.
The result was less about producing a final “best prompt” and more about making the system easier to iterate on intelligently. That is what makes the experiment valuable. It treats optimization as a structured engineering process rather than a sequence of ad hoc prompt edits.