TECHNIQUE
Guardrails & Safety
Deterministic rule guards are deployed as narrow validation, filtering, routing, and policy layers around AI outputs—most visibly in code workflows, content evaluation, and document processing.
Use rule-based validators to catch or suppress low-quality model outputs before they enter downstream workflows.
3 of 3 operators with cited rule-guard evidence use deterministic checks as validation or filtering around AI/model outputs.Run explicit rule-based checks for structure, formatting, length, schema, or simple safety conditions in evaluation pipelines.
1 of 3 operators with cited rule-guard evidence explicitly reports structure, formatting, length, schema, and simple safety checks.Use domain-specific rule validators as a second verification layer for LLM findings.
1 of 3 operators with cited rule-guard evidence explicitly reports domain-specific rule validators as a second verification layer.Apply deterministic static filters before model analysis to exclude ineligible or irrelevant inputs.
1 of 3 operators with cited rule-guard evidence explicitly reports static filtering before model analysis.Encode code-migration rules in deterministic artifacts such as DSL rules, AST-based import inspection, custom linters, and steering messages.
1 of 3 operators with cited rule-guard evidence explicitly reports DSL rules, AST inspection, custom linters, and deterministic steering for code migration.Turn recurring model failures into deterministic policy so the same failures stop recurring.
1 of 3 operators with cited rule-guard evidence explicitly describes converting repeated failures into policy.Suppress generated items from categories with historically low value.
1 of 3 operators with cited rule-guard evidence explicitly reports category-based suppression using historical developer value.Log rule-guard outcomes and related evaluation metadata for monitoring, reproducibility, and drift/error analysis.
2 of 3 operators with cited rule-guard evidence explicitly report logging or monitoring guard/evaluation outcomes.All cited operators deploy deterministic rules as part of a larger AI workflow rather than as the only decision mechanism.
All cited operators use the rules to reduce AI failure modes before outputs are accepted, surfaced, or sent downstream.
Operators aim the deterministic rules at different failure surfaces.
APPROACH 01
Content-quality and safety checks: structure, formatting, length, schema validation, and simple input/output safety checks.
APPROACH 02
Code-analysis and optimization checks: static filters, antipattern catalogs, and domain-specific rule validators for LLM findings.
APPROACH 03
Code-migration guards: import-migration DSL rules, AST-guided hint selection, custom linters, and TypeScript/Jest feedback loops.
Operators place rules at different stages of the workflow.
APPROACH 01
Pre-model filtering or prompt scoping: determine eligible files/functions or inject only relevant hints before the model acts.
APPROACH 02
Post-model validation and suppression: validate, score, filter, or suppress model outputs before posting or downstream use.
False positives, hallucinations, or unreliable model outputs remain the recurring reason operators add deterministic guards.
Rules need operational upkeep: operators log failures, monitor drift/error patterns, or convert repeated failures into policy.
Rule guards are not treated as sufficient alone; operators pair them with LLM judges, human review, tests, diagnostics, or downstream manual review.
| Name | Kind | When | Maturity |
|---|---|---|---|
| Post-parse validators in code | pattern | hard constraints enforced after parsing, never delegated to the model | commodity |
| Open Policy Agent | library | rule sets maintained by non-ML teams as versioned policy | established |