ai-following-rules

To enforce constraints on AI outputs, you can programmatically apply hard and soft rules to model generations. This approach uses dspy.Assert to block policy or format violations and dspy.Suggest to steer outputs without halting progress.

Hard constraints in DSPy use dspy.Assert to block violations and retry until the output is compliant, ensuring strict policy adherence. Soft constraints use dspy.Suggest to gently steer model outputs without blocking the generation pipeline.

You can chain multiple validation rules for content safety by modularly composing hard and soft constraints. This allows you to sequence policy checks, apply custom error messages, and use backtracking across multiple steps for deterministic compliance.

Yes, you can use dspy.Assert to enforce hard rules that block format violations and automatically retry AI outputs. This ensures strict format correctness and policy compliance by programmatically validating model responses.

You should use soft guidance via dspy.Suggest when you need to steer AI outputs toward desired constraints without blocking progress or triggering retries. Use hard rules with dspy.Assert only when strict policy compliance and format correctness are mandatory.

Yes, this constraint enforcement approach supports backtracking and optional components as needed. It programmatically applies hard and soft constraints across multiple steps, allowing modular composition for complex content safety and policy validation workflows.