How we used DSPy to turn AI evaluations into better responses in Dash chat
Dropbox uses DSPy to turn AI evaluations into improvements for its Dash chat agent. The process first calibrates LLM judges against human-labeled conversations, then uses those judges to optimize the agent’s system prompt. This feedback loop reduced incomplete answers and token usage while maintaining answer quality. ## The Complexity of Evaluating AI Agents - Agent quality depends on more than the final response: - Understanding user intent - Selecting relevant context - Choosing and using tools - Synthesizing information across documents, messages, and meetings - Handling ambiguity and follow-up turns - Producing grounded, complete answers - Evaluations therefore inspect the full interaction trajectory, not just the output. - Separate evaluations for intent understanding, tool use, context selection, grounding, adaptation, and task completion help identify the source of failures. - Reliable judges were necessary before evaluation results could safely guide agent improvements. ## Calibrating LLM Judges with Human Labels - Dropbox sampled internal chats containing final answers and agent trace logs. - Human reviewers scored five dimensions: - User-intent following - Semantic relevance - Tool calling - Instruction following - Context selection - Reviewers followed a structured process: - Determine whether the agent understood the request. - Check whether it selected appropriate context. - Inspect searches, retrievals, and other tool actions. - Verify that final claims were supported by evidence. - Score relevance, grounding, completeness, and instruction adherence. - Many metrics used a 1–5 scale. - Reviewers also added: - Reasoning notes explaining their scores - Failure codes for issues such as stale evidence, missing context, unsupported claims, incomplete coverage, and poor personalization - These richer annotations helped improve judge prompts while also supporting debugging, error analysis, roadmap planning, and prioritization. ## Using DSPy to Improve Evaluation - DSPy was used to make LLM judges align more closely with human evaluations. - Judges were required to follow a retrospective workflow: - Infer the user’s intent - Inspect the conversation and agent trace - Review supporting evidence - Assess context selection and tool use - Produce scores, failure codes, and reasoning notes - GEPA and MIPROv2, optimization algorithms within DSPy, automatically proposed and tested prompt changes against human-labeled examples. - Optimization supported several scenarios: - Rewriting judge instructions entirely - Adapting a judge to another underlying model - Targeting specific failure modes while preserving the existing evaluation behavior The overall approach creates a scalable improvement loop: human labels calibrate the judges, calibrated judges provide consistent evaluation signals, and those signals guide improvements to the chat agent itself.
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