8bit.tr Journal
Constraint Solving with LLMs: Hybrid Planning Pipelines
How to combine LLMs with constraint solvers for reliable planning and optimization.
Why Constraints Improve Reliability
LLMs can propose plans, but constraint solvers enforce feasibility.
This hybrid approach reduces invalid or risky outputs.
Solver Integration Patterns
Use LLMs to generate candidate plans and constraints.
Validate with a solver before execution.
Use Cases That Benefit
Scheduling, resource allocation, and policy compliance tasks.
Any workflow where feasibility and constraints are strict.
Operational Considerations
Solvers can add latency; route only when needed.
Log solver failures to refine prompt structure.
Evaluation Strategies
Measure feasibility rate and plan quality.
Track fallback rates when solver rejects outputs.
Constraint Modeling
Translate natural language constraints into formal variables.
Use templates to keep constraint formats consistent.
Validate constraint sets before calling the solver.
Limit solver scope to the minimal necessary variables.
Include soft constraints to balance feasibility and quality.
Attach explanations to constraints to improve debugging.
Log constraint revisions to trace failures.
Run unit tests on constraint libraries for stability.
Fallback and Safety
Provide heuristic fallbacks when the solver times out.
Route low-stakes tasks to simpler rule-based validators.
Expose solver confidence scores to downstream systems.
Throttle solver usage under heavy load to protect latency.
Require human approval for high-risk plan execution.
Store rejected plans to improve prompts and constraints.
Use safety constraints to prevent dangerous outputs.
Review solver failures regularly to improve coverage.
Log fallback usage to track solver reliability over time.
Add timeouts per constraint class to avoid worst-case stalls.
Keep a minimal safe plan when constraints are ambiguous.
Alert when solver rejection rates spike unexpectedly.
Tag safety-critical constraints for stricter validation.
Include rollback steps when plans fail mid-execution.
Prefer deterministic solvers for workflows with legal constraints.
Store solver inputs for replay during incident analysis.
Add guardrails against infeasible solver suggestions from prompts.
Use staged execution so partial plans can be verified.
Limit plan length to keep solver complexity bounded.
Capture solver metrics to tune performance over time.
FAQ: Constraint Solving
Do I need a full solver? Start with simple rule-based validators.
Is this overkill? Not for high-stakes planning tasks.
What is the biggest risk? Solver latency affecting user experience.
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