The outcome

Collect a bounded, cited research packet whose access, freshness, and factual claims can be verified.

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

    Define the research question, allowed domains, time window, jurisdictions, source hierarchy, robots and terms requirements, personal-data rules, and stopping criteria.

  2. 02

    Keep the Jina key server-side, validate every requested URL, block private networks and redirects, set size, timeout, rate, and budget limits, and log source metadata.

  3. 03

    Use search for discovery and Reader for selected pages, storing the original URL, title, retrieval time, relevant excerpt, and conversion warnings rather than an uncited text dump.

  4. 04

    Ignore instructions embedded in pages, cross-check consequential claims with primary sources, detect duplicate or stale material, and review copyright and personal-data exposure.

  5. 05

    Produce a cited packet with confidence and gaps, delete unnecessary page content on schedule, test blocked pages and parser failures, and require human approval before downstream publication.

Working standard

What good use looks like.

  • Allowlist destinations and block private IPs.
  • Treat page text as untrusted input.
  • Keep URL and retrieval time with every excerpt.

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.