How to Use AI21 Labs for Structured JSON outputs

This structured json outputs guide shows a practical AI21 Labs path from brief to reviewable output. Lead with RAG answer, use API call, and keep quota aware secondary until the core result is right. Plans: www.ai21.com/pricing. Explore: /explore/ai21-labs.

Below is a full structured json outputs walkthrough. See also /blog/how-to-use-ai21-labs-for-classification-and-labeling, /blog/how-to-use-ai21-labs-for-chat-and-completion-workflows, /blog/how-to-use-ai21-labs-for-enterprise-deployment-and-quotas.

When this workflow is the right job

Pick structured json outputs for a focused AI21 Labs pass. Skip it when jurassic api text generation or summarize and paraphrase endpoints covers the requirement more directly.

Step by step workflow

1. Brief Structured JSON outputs

Write what must stay true for structured json outputs in AI21 Labs before settings or spend.

Brief: Structured JSON outputs
Keep: paraphrase pass from SOURCE
Avoid: invented pricing or features
Success: one reviewable output

2. Open AI21 Labs for Structured JSON outputs

Use the AI21 Labs surface that owns structured json outputs. Do not mix a neighboring workflow in the same pass.

Surface: Structured JSON outputs
Start: schema enforce
Plans: www.ai21.com/pricing

3. Pilot Structured JSON outputs

Run a single structured json outputs pilot. Score clarity, grounding, and whether quota aware still matches.

Pilot: Structured JSON outputs
[ ] SOURCE facts match
[ ] RAG answer clear
[ ] Settings logged

4. Refine Structured JSON outputs

Change one structured json outputs dimension only. Save a template with variables for citation block.

Refine: Structured JSON outputs
Change: meter usage
Keep: SOURCE and low temperature

Practical structured json outputs examples

paraphrase pass

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "paraphrase pass".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for paraphrase pass
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving paraphrase pass.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for paraphrase pass ready for persistence, plus error handling notes.

embedding index

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "embedding index".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for embedding index
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving embedding index.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for embedding index ready for persistence, plus error handling notes.

JSON schema

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "JSON schema".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for JSON schema
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving JSON schema.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for JSON schema ready for persistence, plus error handling notes.

classification

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "classification".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for classification
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving classification.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for classification ready for persistence, plus error handling notes.

long doc chunk

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "long doc chunk".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for long doc chunk
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving long doc chunk.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for long doc chunk ready for persistence, plus error handling notes.

quota check

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "quota check".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for quota check
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving quota check.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for quota check ready for persistence, plus error handling notes.

enterprise deploy

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "enterprise deploy".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for enterprise deploy
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving enterprise deploy.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for enterprise deploy ready for persistence, plus error handling notes.

RAG answer

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "RAG answer".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for RAG answer
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving RAG answer.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for RAG answer ready for persistence, plus error handling notes.

label taxonomy

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "label taxonomy".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for label taxonomy
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving label taxonomy.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for label taxonomy ready for persistence, plus error handling notes.

temperature low

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "temperature low".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for temperature low
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving temperature low.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for temperature low ready for persistence, plus error handling notes.

parse validate

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "parse validate".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for parse validate
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving parse validate.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for parse validate ready for persistence, plus error handling notes.

citation block

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "citation block".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for citation block
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving citation block.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for citation block ready for persistence, plus error handling notes.

chat completion

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "chat completion".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for chat completion
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving chat completion.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for chat completion ready for persistence, plus error handling notes.

batch jobs

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "batch jobs".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for batch jobs
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving batch jobs.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for batch jobs ready for persistence, plus error handling notes.

safety filter

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "safety filter".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for safety filter
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving safety filter.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for safety filter ready for persistence, plus error handling notes.

token budget

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "token budget".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for token budget
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving token budget.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for token budget ready for persistence, plus error handling notes.

retry policy

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "retry policy".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for retry policy
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving retry policy.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for retry policy ready for persistence, plus error handling notes.

eval harness

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "eval harness".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for eval harness
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving eval harness.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for eval harness ready for persistence, plus error handling notes.

prompt version

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "prompt version".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for prompt version
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving prompt version.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for prompt version ready for persistence, plus error handling notes.

Jurassic completion

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "Jurassic completion".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for Jurassic completion
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving Jurassic completion.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for Jurassic completion ready for persistence, plus error handling notes.

summarize endpoint

Scenario:
An engineer is implementing AI21 Labs structured json outputs for the task "summarize endpoint".

Objective:
Call the API with schema/temperature discipline, validate outputs, and avoid raw model writes to production stores.

Inputs:
- Endpoint + model notes for summarize endpoint
- JSON schema or output contract
- Temperature / token budget
- Quota check before batch

Workflow:
Build request → Call structured json outputs → Validate schema → Persist only validated fields → Log request id

Requirements:
- Validate JSON before side effects.
- Use low temperature for routing/classify jobs involving summarize endpoint.
- Check quota before batches; confirm on official pricing pages.
- Never write raw model text into production DBs.

Expected output:
A validated structured json outputs response for summarize endpoint ready for persistence, plus error handling notes.

How to improve structured json outputs

Lift structured json outputs consistency by reusing the same API call vocabulary across related AI21 Labs jobs.

Harden structured json outputs by testing an empty or incomplete eval harness input before trusting AI21 Labs defaults.

Cut noise from structured json outputs by removing extra adjectives while preserving prompt version in AI21 Labs.

Raise structured json outputs quality by insisting on embed then search before any style debate in AI21 Labs.

Make structured json outputs easier to review by labeling summarize endpoint fields that must never change in AI21 Labs.

Speed structured json outputs iteration by cloning the last good AI21 Labs run and altering only set temperature.

Stabilize structured json outputs by pinning low temperature after embedding index is approved in AI21 Labs.

Reduce structured json outputs rework by rejecting drafts that invent claims about JSON schema in AI21 Labs.

Prompting and usage guidance

Lead structured json outputs with constraints: channel, length, and forbidden claims inside AI21 Labs.

Separate creative instructions from SOURCE so structured json outputs stays grounded in AI21 Labs.

Request structured json outputs output as a checklist first when stakeholders need approval gates.

For structured json outputs, describe enterprise deploy with concrete nouns, then add eval driven only if the draft already works.

Ask AI21 Labs to list assumptions made during structured json outputs before you accept the draft.

Limitations to respect

Do not invent credit costs for structured json outputs; read live numbers on www.ai21.com/pricing.

AI21 Labs can be wrong. Treat structured json outputs as provisional until review.

Connected apps used in structured json outputs may throttle traffic independently of AI21 Labs.

If documentation is silent on a structured json outputs claim, leave it out rather than guessing.

Practical tips for this workflow

For structured json outputs, capture a before and after artifact of JSON schema every time AI21 Labs settings change.

Teach structured json outputs operators where AI21 Labs controls for meter usage live so fixes are not person dependent.

Prefer idempotent structured json outputs steps when AI21 Labs reruns are likely after a failed retry policy pass.

Rank structured json outputs examples by reuse frequency, putting JSON schema patterns that win reviews at the top.

Close each structured json outputs session by noting the next set temperature tweak to try in AI21 Labs.

When stakeholders want premium structured json outputs polish, change quota aware before you rewrite retry policy facts.

Budget a second structured json outputs pass focused on edge cases around JSON schema, not only the happy path in AI21 Labs.

Use official AI21 Labs terminology for structured json outputs in SOPs so support recognizes schema enforce requests.

Keep a structured json outputs checklist beside AI21 Labs so reviewers know which retry policy details stayed locked.

Pilot structured json outputs on a tiny sample before spending AI21 Labs credits or executions on a full batch centered on JSON schema.

AI21 Labs structured json outputs note: after docs model name, recheck parse validate against SOURCE and confirm ops durable still matches the brief.

AI21 Labs structured json outputs note: after API call, recheck citation block against SOURCE and confirm API precise still matches the brief.

AI21 Labs structured json outputs note: after validate JSON, recheck chat completion against SOURCE and confirm RAG grounded still matches the brief.

Common mistakes

• Starting structured json outputs without SOURCE facts in AI21 Labs
• Treating marketing blogs as official AI21 Labs limits
• Regenerating everything when one structured json outputs section failed
• Leaving credentials in structured json outputs node fields instead of vaults
• Promising delivery dates before checking AI21 Labs plan access
• Skipping the human read on customer facing structured json outputs drafts

After this structured json outputs guide, continue with /blog/how-to-use-ai21-labs-for-classification-and-labeling, /blog/how-to-use-ai21-labs-for-chat-and-completion-workflows, /blog/how-to-use-ai21-labs-for-enterprise-deployment-and-quotas. Start again at /explore/ai21-labs if you need the full AI21 Labs map.