How to Use AI21 Labs for Summarize and paraphrase endpoints

This summarize and paraphrase endpoints guide shows a practical AI21 Labs path from brief to reviewable output. Lead with token budget, use fallback template, and keep quota aware secondary until the core result is right. Plans: www.ai21.com/pricing. Explore: /explore/ai21-labs.

Below is a full summarize and paraphrase endpoints walkthrough. See also /blog/how-to-use-ai21-labs-for-embedding-and-rag-pipelines, /blog/how-to-use-ai21-labs-for-long-context-document-tasks, /blog/how-to-use-ai21-labs-for-structured-json-outputs.

When this workflow is the right job

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

Step by step workflow

1. Brief Summarize and paraphrase endpoints

Write what must stay true for summarize and paraphrase endpoints in AI21 Labs before settings or spend.

Brief: Summarize and paraphrase endpoints
Keep: citation block from SOURCE
Avoid: invented pricing or features
Success: one reviewable output

2. Open AI21 Labs for Summarize and paraphrase endpoints

Use the AI21 Labs surface that owns summarize and paraphrase endpoints. Do not mix a neighboring workflow in the same pass.

Surface: Summarize and paraphrase endpoints
Start: log request id
Plans: www.ai21.com/pricing

3. Pilot Summarize and paraphrase endpoints

Run a single summarize and paraphrase endpoints pilot. Score clarity, grounding, and whether RAG grounded still matches.

Pilot: Summarize and paraphrase endpoints
[ ] SOURCE facts match
[ ] prompt version clear
[ ] Settings logged

4. Refine Summarize and paraphrase endpoints

Change one summarize and paraphrase endpoints dimension only. Save a template with variables for embedding index.

Refine: Summarize and paraphrase endpoints
Change: set temperature
Keep: SOURCE and schema strict

Practical summarize and paraphrase endpoints examples

citation block

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for citation block ready for persistence, plus error handling notes.

chat completion

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for chat completion ready for persistence, plus error handling notes.

batch jobs

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for batch jobs ready for persistence, plus error handling notes.

safety filter

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for safety filter ready for persistence, plus error handling notes.

token budget

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for token budget ready for persistence, plus error handling notes.

retry policy

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for retry policy ready for persistence, plus error handling notes.

eval harness

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for eval harness ready for persistence, plus error handling notes.

prompt version

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for prompt version ready for persistence, plus error handling notes.

Jurassic completion

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for Jurassic completion ready for persistence, plus error handling notes.

summarize endpoint

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for summarize endpoint ready for persistence, plus error handling notes.

paraphrase pass

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for paraphrase pass ready for persistence, plus error handling notes.

embedding index

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for embedding index ready for persistence, plus error handling notes.

JSON schema

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for JSON schema ready for persistence, plus error handling notes.

classification

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for classification ready for persistence, plus error handling notes.

long doc chunk

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for long doc chunk ready for persistence, plus error handling notes.

quota check

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for quota check ready for persistence, plus error handling notes.

enterprise deploy

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for enterprise deploy ready for persistence, plus error handling notes.

RAG answer

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for RAG answer ready for persistence, plus error handling notes.

label taxonomy

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for label taxonomy ready for persistence, plus error handling notes.

temperature low

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for temperature low ready for persistence, plus error handling notes.

parse validate

Scenario:
An engineer is implementing AI21 Labs summarize and paraphrase endpoints 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 summarize and paraphrase endpoints → 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 summarize and paraphrase endpoints response for parse validate ready for persistence, plus error handling notes.

How to improve summarize and paraphrase endpoints

Make summarize and paraphrase endpoints easier to review by labeling chat completion fields that must never change in AI21 Labs.

Speed summarize and paraphrase endpoints iteration by cloning the last good AI21 Labs run and altering only log request id.

Stabilize summarize and paraphrase endpoints by pinning low temperature after safety filter is approved in AI21 Labs.

Reduce summarize and paraphrase endpoints rework by rejecting drafts that invent claims about token budget in AI21 Labs.

Improve summarize and paraphrase endpoints handoffs by recording which AI21 Labs control produced the retry policy result.

Strengthen summarize and paraphrase endpoints by adding a second reader who only checks eval harness spelling and facts in AI21 Labs.

Lift summarize and paraphrase endpoints consistency by reusing the same chunk text vocabulary across related AI21 Labs jobs.

Harden summarize and paraphrase endpoints by testing an empty or incomplete Jurassic completion input before trusting AI21 Labs defaults.

Prompting and usage guidance

Lead summarize and paraphrase endpoints with constraints: channel, length, and forbidden claims inside AI21 Labs.

Separate creative instructions from SOURCE so summarize and paraphrase endpoints stays grounded in AI21 Labs.

Request summarize and paraphrase endpoints output as a checklist first when stakeholders need approval gates.

For summarize and paraphrase endpoints, describe safety filter with concrete nouns, then add eval driven only if the draft already works.

Ask AI21 Labs to list assumptions made during summarize and paraphrase endpoints before you accept the draft.

Limitations to respect

Do not invent credit costs for summarize and paraphrase endpoints; read live numbers on www.ai21.com/pricing.

AI21 Labs can be wrong. Treat summarize and paraphrase endpoints as provisional until review.

Connected apps used in summarize and paraphrase endpoints may throttle traffic independently of AI21 Labs.

If documentation is silent on a summarize and paraphrase endpoints claim, leave it out rather than guessing.

Practical tips for this workflow

Document AI21 Labs UI labels used for summarize and paraphrase endpoints so handoffs about embedding index do not rely on memory.

Store winning summarize and paraphrase endpoints settings as a template with variables only for label taxonomy fields in AI21 Labs.

Approve SOURCE facts before spending budget on summarize and paraphrase endpoints variants that mention token budget in AI21 Labs.

Pair customer facing summarize and paraphrase endpoints exports with a human read that checks invented claims about embedding index.

Log AI21 Labs run identifiers for summarize and paraphrase endpoints so ops can replay embed then search failures without guessing.

Split oversized summarize and paraphrase endpoints work into smaller API call passes rather than one overloaded AI21 Labs request.

Review summarize and paraphrase endpoints while context is fresh; delayed checks miss ops durable mismatches on embedding index.

If summarize and paraphrase endpoints touches compliance language about label taxonomy, lock verbatim strings outside AI21 Labs first.

Retire summarize and paraphrase endpoints templates when AI21 Labs docs change names or gates for token budget workflows.

For summarize and paraphrase endpoints, capture a before and after artifact of embedding index every time AI21 Labs settings change.

AI21 Labs summarize and paraphrase endpoints note: after chunk text, recheck embedding index against SOURCE and confirm API precise still matches the brief.

Common mistakes

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

After this summarize and paraphrase endpoints guide, continue with /blog/how-to-use-ai21-labs-for-embedding-and-rag-pipelines, /blog/how-to-use-ai21-labs-for-long-context-document-tasks, /blog/how-to-use-ai21-labs-for-structured-json-outputs. Start again at /explore/ai21-labs if you need the full AI21 Labs map.