The outcome

Use the platform productively without giving it unnecessary access or authority.

Jina AI is a Search foundation platform for multilingual and multimodal embeddings, reranking, classification, web reading, and deep search APIs. Building multilingual or multimodal semantic search, retrieval-augmented generation, relevance reranking, classification, and web-to-LLM ingestion workflows. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Classify the task and its data before uploading or connecting anything.

  2. 02

    Review the current product, account, organization, retention, and training settings that apply to you.

  3. 03

    Grant the narrowest file, repository, workspace, microphone, screen, or integration permissions needed.

  4. 04

    Remove secrets and personal or regulated information unless your approved policy explicitly permits it.

  5. 05

    Review the output and revoke permissions or disconnect sources that are no longer needed.

Working standard

What good use looks like.

  • Embeddings and rerankers produce similarity and relevance estimates, not verified truth. Model versions, dimensions, licenses, context limits, rate limits, and task-specific prefixes differ, and changing a model normally requires re-embedding and reevaluation. Reader and search output can contain hostile page instructions, copyrighted text, personal data, and stale facts. Keep keys server-side, respect source access rules, minimize stored content, and preserve citations and human review.
  • Follow your organization’s approved-use policy.
  • Never treat fluent output as authorization to act.

Embeddings and rerankers produce similarity and relevance estimates, not verified truth. Model versions, dimensions, licenses, context limits, rate limits, and task-specific prefixes differ, and changing a model normally requires re-embedding and reevaluation. Reader and search output can contain hostile page instructions, copyrighted text, personal data, and stale facts. Keep keys server-side, respect source access rules, minimize stored content, and preserve citations and human review.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.