Output Post-processing

Benched.ai Editorial Team

Output post-processing transforms raw model generations into the final form delivered to users or downstream systems.

Common Steps

Step	Purpose	Example Tool
Schema validation	Ensure JSON matches spec	`pydantic`, `jsonschema`
Safety filter	Remove PII, toxicity	OpenAI moderation, Perspective API
Formatting	Trim whitespace, capitalize	regex, `black` for code
Unit conversion	Normalize units & currency	Pint
Linking & markup	Add Markdown links	custom scripts

Design Trade-offs

Strict validators reduce errors but may over-reject valid replies.
Aggressive filtering can censor creative language.
Extra transforms add latency; batch where possible.

Current Trends (2025)

Streaming post-processors handle partial JSON shards as tokens arrive.
Explainability tags (source_sentence) added for citation tracking.
Inline code formatters auto-fix Python before execution.

Implementation Tips

Keep raw model output for audit before mutations.
Run post-processing in isolated sandbox to avoid code injection.
Log distinct error types (schema, safety) to guide prompt fixes.