DSPy takes a fundamentally different approach to LLM programming. Instead of writing prompts manually, you define signatures and let the optimizer find effective prompts. This creates unique safety challenges because the prompts your system uses in production may not be the ones you wrote.
In traditional LLM applications, you control the exact prompts sent to the model. You can review them, test them, and verify they include safety instructions. In DSPy, the optimizer generates and modifies prompts based on training examples and metrics. Optimized prompts might inadvertently drop safety instructions or create formulations that are more susceptible to injection.
Define safety constraints as part of your DSPy metric function. The metric should penalize outputs that violate safety policies, not just optimize for task performance. Run Authensor's content scanner on program outputs during optimization and incorporate safety scores into the metric.
This teaches the optimizer that safe outputs are part of what "good" means, aligning optimization with safety requirements.
Regardless of what the optimizer produces, wrap DSPy program execution with Authensor's runtime checks. Scan both the optimized prompts (before they reach the model) and the model's outputs (before they reach the user).
Since optimized prompts can change after each optimization run, automated scanning is more reliable than manual review. Set up a CI step that scans newly optimized prompts against your safety policy before deploying them.
Add safety-relevant fields to your DSPy signatures. Include output fields that force the model to declare its confidence level or flag potentially harmful content. These fields become optimization targets alongside your primary task fields.
After deploying an optimized program, monitor its safety metrics closely. Optimization on a training set does not guarantee safety on the full distribution of production inputs. Authensor's Sentinel engine tracks safety violation rates over time and alerts on increases.
Store each optimized program version with its safety evaluation results. If a new optimization run produces a program with degraded safety metrics, automatically roll back to the previous version. Treat safety regression the same as performance regression in your deployment pipeline.
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