Agentifact assessment — independently scored, not sponsored. Last verified Apr 2, 2026.
Guidance
Microsoft's templating language for LLM output control. Interleaves generation and prompting with constrained decoding — ensures structured outputs, regex-constrained generation, and stateful chat with guaranteed token-level control.
Significant concerns — proceed carefully
You need guaranteed structured outputs like JSON or regex-constrained fields from LLMs without parsing failures or retries.
2x faster inference, 30-50% cost savings vs prompting; rock-solid on structure but Handlebars syntax ({{ }} ) trips up Python f-string users.
Prompt chaining or verbose prompts make your agent slow, costly, and inconsistent for complex flows.
Outperforms chaining on BigBench (76% vs 63%); elegant for RPG generators or data pipelines, but linear execution limits non-sequential tasks.
Handlebars template learning curve
Double braces {{ }} for vars (opposite Python f-strings) and linear execution only—no branching or loops like full programming.
Guidance prioritizes template simplicity and broad LLM support over LMQL's query-language syntax.
You want Handlebars-style ease and Microsoft ecosystem integration for structured gen.
You need full query language with branching for complex logic flows.
Double-brace gotcha
Templates use {{var}} not {var}; literal braces need single {}. Convert with utils or expect initial syntax errors.
Trust Breakdown
What It Actually Does
Guidance lets you program language models to produce structured outputs, like JSON or specific formats, by mixing prompts with rules that control what they generate word by word. This ensures reliable, predictable results instead of random text.
Microsoft's templating language for LLM output control. Interleaves generation and prompting with constrained decoding — ensures structured outputs, regex-constrained generation, and stateful chat with guaranteed token-level control.
Fit Assessment
Best for
- ✓code-generation
Connection Patterns
Blueprints that include this tool: