exploreAIExplore
How to Use AI21 Labs for Long context document tasks
Learn AI21 Labs long context document tasks with step by step workflows, realistic examples, and verified plan notes.
AI21 Labs works well for long context document tasks when you run it like production work: locked brief, SOURCE facts, then embed then search focused on parse validate. Confirm live plans on www.ai21.com/pricing. Start at /explore/ai21-labs.
This guide focuses on long context document tasks in detail. Related AI21 Labs articles: /blog/how-to-use-ai21-labs-for-structured-json-outputs, /blog/how-to-use-ai21-labs-for-classification-and-labeling, /blog/how-to-use-ai21-labs-for-chat-and-completion-workflows.
When this workflow is the right job
Use long context document tasks when the deliverable is specifically this AI21 Labs job. Switch to jurassic api text generation when that workflow already owns the asset.
Step by step workflow
1. Brief Long context document tasks
Write what must stay true for long context document tasks in AI21 Labs before settings or spend.
Brief: Long context document tasks Keep: long doc chunk from SOURCE Avoid: invented pricing or features Success: one reviewable output
2. Open AI21 Labs for Long context document tasks
Use the AI21 Labs surface that owns long context document tasks. Do not mix a neighboring workflow in the same pass.
Surface: Long context document tasks Start: API call Plans: www.ai21.com/pricing
3. Pilot Long context document tasks
Run a single long context document tasks pilot. Score clarity, grounding, and whether low temperature still matches.
Pilot: Long context document tasks [ ] SOURCE facts match [ ] citation block clear [ ] Settings logged
4. Refine Long context document tasks
Change one long context document tasks dimension only. Save a template with variables for token budget.
Refine: Long context document tasks Change: fallback template Keep: SOURCE and quota aware
Practical long context document tasks examples
long doc chunk
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for long doc chunk ready for persistence, plus error handling notes.
quota check
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for quota check ready for persistence, plus error handling notes.
enterprise deploy
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for enterprise deploy ready for persistence, plus error handling notes.
RAG answer
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for RAG answer ready for persistence, plus error handling notes.
label taxonomy
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for label taxonomy ready for persistence, plus error handling notes.
temperature low
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for temperature low ready for persistence, plus error handling notes.
parse validate
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for parse validate ready for persistence, plus error handling notes.
citation block
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for citation block ready for persistence, plus error handling notes.
chat completion
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for chat completion ready for persistence, plus error handling notes.
batch jobs
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for batch jobs ready for persistence, plus error handling notes.
safety filter
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for safety filter ready for persistence, plus error handling notes.
token budget
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for token budget ready for persistence, plus error handling notes.
retry policy
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for retry policy ready for persistence, plus error handling notes.
eval harness
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for eval harness ready for persistence, plus error handling notes.
prompt version
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for prompt version ready for persistence, plus error handling notes.
Jurassic completion
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for Jurassic completion ready for persistence, plus error handling notes.
summarize endpoint
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for summarize endpoint ready for persistence, plus error handling notes.
paraphrase pass
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for paraphrase pass ready for persistence, plus error handling notes.
embedding index
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for embedding index ready for persistence, plus error handling notes.
JSON schema
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for JSON schema ready for persistence, plus error handling notes.
classification
Scenario: An engineer is implementing AI21 Labs long context document tasks 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 long context document tasks → 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 long context document tasks response for classification ready for persistence, plus error handling notes.
How to improve long context document tasks
Improve long context document tasks handoffs by recording which AI21 Labs control produced the token budget result.
Strengthen long context document tasks by adding a second reader who only checks retry policy spelling and facts in AI21 Labs.
Lift long context document tasks consistency by reusing the same validate JSON vocabulary across related AI21 Labs jobs.
Harden long context document tasks by testing an empty or incomplete prompt version input before trusting AI21 Labs defaults.
Cut noise from long context document tasks by removing extra adjectives while preserving Jurassic completion in AI21 Labs.
Raise long context document tasks quality by insisting on meter usage before any style debate in AI21 Labs.
Make long context document tasks easier to review by labeling paraphrase pass fields that must never change in AI21 Labs.
Speed long context document tasks iteration by cloning the last good AI21 Labs run and altering only schema enforce.
Prompting and usage guidance
Name the long context document tasks job, the audience, and one measurable success check before opening AI21 Labs.
Paste only verified facts under SOURCE so AI21 Labs cannot invent details during long context document tasks.
Specify the long context document tasks deliverable shape up front, such as scenes, bullets, rows, or a signed note.
Call out fixed temperature low details versus flexible docs model name choices for long context document tasks.
Close with a review line that asks AI21 Labs to flag unsupported claims for long context document tasks.
Limitations to respect
Check AI21 Labs plan gates for long context document tasks on www.ai21.com/pricing before you promise timelines.
Keep long context document tasks drafts unpublished until a human confirms SOURCE facts.
Plan and region differences can change long context document tasks availability. Prefer official AI21 Labs docs.
Beta or preview labels on AI21 Labs mean you should pilot long context document tasks before wide rollout.
Practical tips for this workflow
Review long context document tasks while context is fresh; delayed checks miss eval driven mismatches on retry policy.
If long context document tasks touches compliance language about JSON schema, lock verbatim strings outside AI21 Labs first.
Retire long context document tasks templates when AI21 Labs docs change names or gates for temperature low workflows.
For long context document tasks, capture a before and after artifact of retry policy every time AI21 Labs settings change.
Teach long context document tasks operators where AI21 Labs controls for fallback template live so fixes are not person dependent.
Prefer idempotent long context document tasks steps when AI21 Labs reruns are likely after a failed temperature low pass.
Rank long context document tasks examples by reuse frequency, putting retry policy patterns that win reviews at the top.
Close each long context document tasks session by noting the next docs model name tweak to try in AI21 Labs.
When stakeholders want premium long context document tasks polish, change eval driven before you rewrite temperature low facts.
Budget a second long context document tasks pass focused on edge cases around retry policy, not only the happy path in AI21 Labs.
Common mistakes
- Skipping a written brief before starting long context document tasks in AI21 Labs
- Inventing pricing, credits, or features not confirmed on official AI21 Labs pages
- Scaling long context document tasks volume before one successful pilot
- Mixing a different AI21 Labs workflow into the same long context document tasks session
- Ignoring plan gates while scheduling long context document tasks deadlines
- Publishing long context document tasks output without stakeholder review
For more on long context document tasks, see /blog/how-to-use-ai21-labs-for-structured-json-outputs, /blog/how-to-use-ai21-labs-for-classification-and-labeling, /blog/how-to-use-ai21-labs-for-chat-and-completion-workflows. Hub: /explore/ai21-labs.