How to Use AI21 Labs for Embedding and RAG pipelines

Teams get better embedding and rag pipelines results in AI21 Labs by constraining the job early. Anchor on temperature low, choose one chunk text, and verify claims against SOURCE. Check www.ai21.com/pricing for current plan details. Open /explore/ai21-labs.

Read this for embedding and rag pipelines only. Neighboring AI21 Labs guides: /blog/how-to-use-ai21-labs-for-long-context-document-tasks, /blog/how-to-use-ai21-labs-for-structured-json-outputs, /blog/how-to-use-ai21-labs-for-classification-and-labeling.

When this workflow is the right job

Embedding and RAG pipelines 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 Embedding and RAG pipelines

Write what must stay true for embedding and rag pipelines in AI21 Labs before settings or spend.

Brief: Embedding and RAG pipelines
Keep: long doc chunk from SOURCE
Avoid: invented pricing or features
Success: one reviewable output

2. Open AI21 Labs for Embedding and RAG pipelines

Use the AI21 Labs surface that owns embedding and rag pipelines. Do not mix a neighboring workflow in the same pass.

Surface: Embedding and RAG pipelines
Start: API call
Plans: www.ai21.com/pricing

3. Pilot Embedding and RAG pipelines

Run a single embedding and rag pipelines pilot. Score clarity, grounding, and whether low temperature still matches.

Pilot: Embedding and RAG pipelines
[ ] SOURCE facts match
[ ] citation block clear
[ ] Settings logged

4. Refine Embedding and RAG pipelines

Change one embedding and rag pipelines dimension only. Save a template with variables for token budget.

Refine: Embedding and RAG pipelines
Change: fallback template
Keep: SOURCE and quota aware

Practical embedding and rag pipelines examples

long doc chunk

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for long doc chunk ready for persistence, plus error handling notes.

quota check

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for quota check ready for persistence, plus error handling notes.

enterprise deploy

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for enterprise deploy ready for persistence, plus error handling notes.

RAG answer

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for RAG answer ready for persistence, plus error handling notes.

label taxonomy

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for label taxonomy ready for persistence, plus error handling notes.

temperature low

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for temperature low ready for persistence, plus error handling notes.

parse validate

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for parse validate ready for persistence, plus error handling notes.

citation block

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for citation block ready for persistence, plus error handling notes.

chat completion

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for chat completion ready for persistence, plus error handling notes.

batch jobs

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for batch jobs ready for persistence, plus error handling notes.

safety filter

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for safety filter ready for persistence, plus error handling notes.

token budget

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for token budget ready for persistence, plus error handling notes.

retry policy

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for retry policy ready for persistence, plus error handling notes.

eval harness

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for eval harness ready for persistence, plus error handling notes.

prompt version

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for prompt version ready for persistence, plus error handling notes.

Jurassic completion

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for Jurassic completion ready for persistence, plus error handling notes.

summarize endpoint

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

paraphrase pass

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

embedding index

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

JSON schema

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for JSON schema ready for persistence, plus error handling notes.

classification

Scenario:
An engineer is implementing AI21 Labs embedding and rag pipelines 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 embedding and rag pipelines → 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 embedding and rag pipelines response for classification ready for persistence, plus error handling notes.

How to improve embedding and rag pipelines

Stabilize embedding and rag pipelines by pinning quota aware after parse validate is approved in AI21 Labs.

Reduce embedding and rag pipelines rework by rejecting drafts that invent claims about citation block in AI21 Labs.

Improve embedding and rag pipelines handoffs by recording which AI21 Labs control produced the chat completion result.

Strengthen embedding and rag pipelines by adding a second reader who only checks batch jobs spelling and facts in AI21 Labs.

Lift embedding and rag pipelines consistency by reusing the same fallback template vocabulary across related AI21 Labs jobs.

Harden embedding and rag pipelines by testing an empty or incomplete token budget input before trusting AI21 Labs defaults.

Cut noise from embedding and rag pipelines by removing extra adjectives while preserving retry policy in AI21 Labs.

Raise embedding and rag pipelines quality by insisting on validate JSON before any style debate in AI21 Labs.

Prompting and usage guidance

Frame embedding and rag pipelines as a production ticket: owner, due date, and definition of done in AI21 Labs.

Block invented metrics by supplying SOURCE numbers that embedding and rag pipelines must not exceed.

Tell AI21 Labs whether embedding and rag pipelines needs options or a single best draft.

Anchor validate JSON language to label taxonomy so embedding and rag pipelines stays coherent in AI21 Labs.

Require a final pass that compares embedding and rag pipelines output to SOURCE line by line.

Limitations to respect

Commercial rights for embedding and rag pipelines depend on your AI21 Labs plan. Confirm on www.ai21.com/pricing.

Human oversight remains required for customer facing embedding and rag pipelines work.

Feature names in AI21 Labs change. Revalidate embedding and rag pipelines SOPs after product updates.

Avoid third party blogs as the source of truth for embedding and rag pipelines limits.

Practical tips for this workflow

Pair customer facing embedding and rag pipelines exports with a human read that checks invented claims about quota check.

Log AI21 Labs run identifiers for embedding and rag pipelines so ops can replay schema enforce failures without guessing.

Split oversized embedding and rag pipelines work into smaller embed then search passes rather than one overloaded AI21 Labs request.

Review embedding and rag pipelines while context is fresh; delayed checks miss quota aware mismatches on quota check.

If embedding and rag pipelines touches compliance language about chat completion, lock verbatim strings outside AI21 Labs first.

Retire embedding and rag pipelines templates when AI21 Labs docs change names or gates for Jurassic completion workflows.

For embedding and rag pipelines, capture a before and after artifact of quota check every time AI21 Labs settings change.

Teach embedding and rag pipelines operators where AI21 Labs controls for fallback template live so fixes are not person dependent.

Prefer idempotent embedding and rag pipelines steps when AI21 Labs reruns are likely after a failed Jurassic completion pass.

Rank embedding and rag pipelines examples by reuse frequency, putting quota check patterns that win reviews at the top.

AI21 Labs embedding and rag pipelines note: after validate JSON, recheck embedding index against SOURCE and confirm enterprise governed still matches the brief.

Common mistakes

• Vague embedding and rag pipelines goals with no success metric in AI21 Labs
• Assuming beta AI21 Labs features are production ready for embedding and rag pipelines
• Batching embedding and rag pipelines before a clean pilot lands
• Changing five variables at once during embedding and rag pipelines refinement
• Forgetting to log settings used for the winning embedding and rag pipelines run
• Shipping embedding and rag pipelines with invented testimonials or metrics

Embedding and RAG pipelines cross links: /blog/how-to-use-ai21-labs-for-long-context-document-tasks, /blog/how-to-use-ai21-labs-for-structured-json-outputs, /blog/how-to-use-ai21-labs-for-classification-and-labeling. Broader AI21 Labs context stays at /explore/ai21-labs.