Guarded Generation
LLM outputs constrained by schemas, validations, or allow/deny lists to keep responses safe and on-brand.
Guarded generation constrains LLM outputs with schemas, validators, and allow/deny lists so responses stay structured and safe. It reduces hallucinations and policy violations.
Used in chatbots, content drafting, code generation, and data extraction, it forces outputs to match expected formats and business rules before sending them downstream.
It fits into workflows as the generation step wrapped by validators. The result is more reliable outputs that require fewer human edits and are safer to automate.
Frequently Asked Questions
How do I enforce structure in outputs?
Use JSON schemas, function calling, or output parsers. Reject or regenerate when structure is invalid.
What if the model keeps breaking the schema?
Simplify the schema, add explicit examples, and tighten prompts. Fall back to smaller steps if needed.
How do I apply allow/deny lists?
Check outputs post-generation and regenerate when blocked terms appear. Maintain lists per use case and review regularly.
Can I combine guarded generation with retrieval?
Yes—retrieve context, then enforce schema and policy checks. This grounds outputs and keeps them compliant.
Does guarded generation increase latency?
Slightly, due to validation and occasional retries. Keep schemas minimal and cache prompts to reduce cost.
How do I measure quality?
Track validation failure rate, regeneration count, human edit rate, and downstream error reduction.
What safety checks should I add?
PII redaction, toxicity filters, fact checks against sources, and limits on actions the output can trigger.
Can guarded generation handle multi-turn chats?
Yes—apply validation per turn and carry forward sanitized context. Reset or summarize history to avoid prompt bloat.
How often should I update schemas?
When business requirements change or failure patterns emerge. Version schemas and roll out gradually.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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