Dev Tutorial: Using LLMs Sparingly in a Ride-Share Zone-Balancing Agent

A developer tutorial series on DEV Community details how to integrate a large language model into a ride-share zone-balancing agent built with LangGraph, now in its second of five parts. The core insight is that LLMs are used for only two narrowly defined tasks: parsing free-text operational notes into structured flags, and generating plain-English explanations for human reviewers. All policy selection logic remains purely deterministic Python code carried over from Part 1, deliberately kept away from the LLM to avoid unreliable outputs. The author explains that an earlier ReAct-loop approach, where the LLM selected policies itself, failed because language models generate plausible text rather than reliably computing numeric comparisons. Five local LLM models are benchmarked side by side to evaluate which performs adequately even within these limited, well-defined roles.