# Build structured AI extraction with a held-out evaluation: tool stack

> Extract typed fields from approved documents and compare accuracy, retries, latency, and cost before connecting the result to business systems.

- Canonical: https://gptnavi.com/stacks/structured-ai-extraction-with-held-out-evaluation-stack
- Estimated monthly cost: Open-source libraries; model calls, optimization runs, engineering time, and hosting are not free.
- Last materially updated: 2026-09-06
- Best for: AI application developers

## Problems this stack solves

- Supplier email intake
- Document-to-draft-record processing

## Recommended tools

### Application framework: choose by language

Recommended: Mastra, Agno

Why: Use Mastra for TypeScript or Agno for Python only when orchestration is needed.

### Validation and optional optimization

Recommended: Instructor, DSPy

Why: Validate structure first; optimize only when a measured baseline justifies it.

### Model hosting

Recommended: Fireworks AI

Why: Benchmark a compatible model with explicit usage caps.

## Beginner setup plan

1. Define field types, optional values, and source-span requirements. Label fifty authorized examples, holding back twenty untouched examples for evaluation. Include missing fields, conflicting dates, and hostile instructions as data.
2. Use Instructor in a small Python service to validate structured output from a compatible model endpoint, such as Fireworks AI after checking model support. Set retry, timeout, and spend limits. Preserve original evidence beside every extracted record.
3. Score field accuracy, unsupported values, failure rate, latency, and cost on the held-out examples. If the baseline needs improvement, use DSPy on training examples only and compare against the unchanged baseline on untouched cases.
4. Use Agno in a Python application or Mastra in a TypeScript application if orchestration is needed; expose the extractor as an explicit service boundary. Keep writes in a human approval queue and monitor changed input formats before wider rollout.

## Included workflows

- https://gptnavi.com/workflows/structured-ai-extraction-with-held-out-evaluation