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How to Use AI21 Labs for Classification and labeling
Learn AI21 Labs classification and labeling with step by step workflows, realistic examples, and verified plan notes.
Teams get better classification and labeling results in AI21 Labs by constraining the job early. Anchor on chat completion, choose one set temperature, and verify claims against SOURCE. Check www.ai21.com/pricing for current plan details. Open /explore/ai21-labs.
Read this for classification and labeling only. Neighboring AI21 Labs guides: /blog/how-to-use-ai21-labs-for-chat-and-completion-workflows, /blog/how-to-use-ai21-labs-for-enterprise-deployment-and-quotas, /blog/how-to-use-ai21-labs-for-jurassic-api-text-generation.
When this workflow is the right job
Classification and labeling is the right AI21 Labs path when stakeholders asked for this outcome by name. Prefer jurassic api text generation if you only need a small adjacent edit.
Step by step workflow
1. Brief Classification and labeling
Write what must stay true for classification and labeling in AI21 Labs before settings or spend.
Brief: Classification and labeling Keep: long doc chunk from SOURCE Avoid: invented pricing or features Success: one reviewable output
2. Open AI21 Labs for Classification and labeling
Use the AI21 Labs surface that owns classification and labeling. Do not mix a neighboring workflow in the same pass.
Surface: Classification and labeling Start: API call Plans: www.ai21.com/pricing
3. Pilot Classification and labeling
Run a single classification and labeling pilot. Score clarity, grounding, and whether low temperature still matches.
Pilot: Classification and labeling [ ] SOURCE facts match [ ] citation block clear [ ] Settings logged
4. Refine Classification and labeling
Change one classification and labeling dimension only. Save a template with variables for token budget.
Refine: Classification and labeling Change: fallback template Keep: SOURCE and quota aware
Practical classification and labeling examples
long doc chunk
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for long doc chunk ready for persistence, plus error handling notes.
quota check
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for quota check ready for persistence, plus error handling notes.
enterprise deploy
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for enterprise deploy ready for persistence, plus error handling notes.
RAG answer
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for RAG answer ready for persistence, plus error handling notes.
label taxonomy
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for label taxonomy ready for persistence, plus error handling notes.
temperature low
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for temperature low ready for persistence, plus error handling notes.
parse validate
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for parse validate ready for persistence, plus error handling notes.
citation block
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for citation block ready for persistence, plus error handling notes.
chat completion
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for chat completion ready for persistence, plus error handling notes.
batch jobs
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for batch jobs ready for persistence, plus error handling notes.
safety filter
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for safety filter ready for persistence, plus error handling notes.
token budget
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for token budget ready for persistence, plus error handling notes.
retry policy
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for retry policy ready for persistence, plus error handling notes.
eval harness
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for eval harness ready for persistence, plus error handling notes.
prompt version
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for prompt version ready for persistence, plus error handling notes.
Jurassic completion
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for Jurassic completion ready for persistence, plus error handling notes.
summarize endpoint
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for summarize endpoint ready for persistence, plus error handling notes.
paraphrase pass
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for paraphrase pass ready for persistence, plus error handling notes.
embedding index
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for embedding index ready for persistence, plus error handling notes.
JSON schema
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for JSON schema ready for persistence, plus error handling notes.
classification
Scenario: An engineer is implementing AI21 Labs classification and labeling 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 classification and labeling → 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 classification and labeling response for classification ready for persistence, plus error handling notes.
How to improve classification and labeling
Cut noise from classification and labeling by removing extra adjectives while preserving long doc chunk in AI21 Labs.
Raise classification and labeling quality by insisting on API call before any style debate in AI21 Labs.
Make classification and labeling easier to review by labeling enterprise deploy fields that must never change in AI21 Labs.
Speed classification and labeling iteration by cloning the last good AI21 Labs run and altering only chunk text.
Stabilize classification and labeling by pinning RAG grounded after label taxonomy is approved in AI21 Labs.
Reduce classification and labeling rework by rejecting drafts that invent claims about temperature low in AI21 Labs.
Improve classification and labeling handoffs by recording which AI21 Labs control produced the parse validate result.
Strengthen classification and labeling by adding a second reader who only checks citation block spelling and facts in AI21 Labs.
Prompting and usage guidance
Frame classification and labeling as a production ticket: owner, due date, and definition of done in AI21 Labs.
Block invented metrics by supplying SOURCE numbers that classification and labeling must not exceed.
Tell AI21 Labs whether classification and labeling needs options or a single best draft.
Anchor meter usage language to citation block so classification and labeling stays coherent in AI21 Labs.
Require a final pass that compares classification and labeling output to SOURCE line by line.
Limitations to respect
Commercial rights for classification and labeling depend on your AI21 Labs plan. Confirm on www.ai21.com/pricing.
Human oversight remains required for customer facing classification and labeling work.
Feature names in AI21 Labs change. Revalidate classification and labeling SOPs after product updates.
Avoid third party blogs as the source of truth for classification and labeling limits.
Practical tips for this workflow
Rank classification and labeling examples by reuse frequency, putting eval harness patterns that win reviews at the top.
Close each classification and labeling session by noting the next docs model name tweak to try in AI21 Labs.
When stakeholders want premium classification and labeling polish, change API precise before you rewrite parse validate facts.
Budget a second classification and labeling pass focused on edge cases around eval harness, not only the happy path in AI21 Labs.
Use official AI21 Labs terminology for classification and labeling in SOPs so support recognizes API call requests.
Keep a classification and labeling checklist beside AI21 Labs so reviewers know which parse validate details stayed locked.
Pilot classification and labeling on a tiny sample before spending AI21 Labs credits or executions on a full batch centered on eval harness.
When classification and labeling fails, change only validate JSON instead of rewriting the entire AI21 Labs brief.
Document AI21 Labs UI labels used for classification and labeling so handoffs about parse validate do not rely on memory.
Store winning classification and labeling settings as a template with variables only for eval harness fields in AI21 Labs.
AI21 Labs classification and labeling note: after validate JSON, recheck embedding index against SOURCE and confirm enterprise governed still matches the brief.
AI21 Labs classification and labeling note: after chunk text, recheck JSON schema against SOURCE and confirm eval driven still matches the brief.
AI21 Labs classification and labeling note: after embed then search, recheck classification against SOURCE and confirm schema strict still matches the brief.
AI21 Labs classification and labeling note: after meter usage, recheck long doc chunk against SOURCE and confirm quota aware still matches the brief.
Common mistakes
- Vague classification and labeling goals with no success metric in AI21 Labs
- Assuming beta AI21 Labs features are production ready for classification and labeling
- Batching classification and labeling before a clean pilot lands
- Changing five variables at once during classification and labeling refinement
- Forgetting to log settings used for the winning classification and labeling run
- Shipping classification and labeling with invented testimonials or metrics
Classification and labeling cross links: /blog/how-to-use-ai21-labs-for-chat-and-completion-workflows, /blog/how-to-use-ai21-labs-for-enterprise-deployment-and-quotas, /blog/how-to-use-ai21-labs-for-jurassic-api-text-generation. Broader AI21 Labs context stays at /explore/ai21-labs.