# Deterministic-LLM Sandwich

> Bracket every LLM call with deterministic checks on both sides — pre-check decides if the model should run, post-check validates the output before it lands.

- **Category**: Agentic AI
- **Subcategory**: Verification & Reflection
- **Canonical URL**: https://designpattern.fyi/patterns/deterministic_llm_sandwich/

---

## Description
**Intent**: Prevent the model from landing unsafe or malformed output by wrapping it in deterministic validation.

**Context**: You use an LLM at a point where wrong output causes real damage — a knitting pattern with a bad stitch count, a DB migration that breaks production, an insurance quote missing a required coverage line. Removing the model entirely isn't an option, but every output is one hallucination away from causing harm.

**Solution**: Three layers. **Pre**: deterministic check decides whether the LLM should run at all (e.g., AST parse must succeed). **LLM**: produces a candidate with a structured-output schema and frozen rubric. **Post**: deterministic re-validation (parse, type-check, run tests). If post fails, return the original input unchanged.


## Use Cases
- LLM output must be checked deterministically before being trusted (AST parse, type-check, test run).
- A pre-check can decide whether the LLM should run at all.
- Returning the original input on post-check failure is acceptable behavior.





## Trade-offs


### Advantages

- Model cannot land an unsafe artifact — post-check is the hard gate.

- Bug fixes go into the deterministic layer where they're testable and repeatable.




### Considerations & Drawbacks

- Building the deterministic checks is the bulk of the engineering work.

- Over-strict post-checks reject valid outputs, degrading the model's utility.







---
**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/deterministic-llm-sandwich/)