The outcome

Return a small, well-ordered evidence set that measurably serves real queries across required languages.

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 users, languages, corpus, source authority, chunking, freshness, access rules, query classes, relevance labels, latency, recall, precision, and cost targets.

  2. 02

    Choose a compatible embedding and reranker, record model versions, dimensions, context, task settings, license, and build a hard evaluation set with judged positives and negatives.

  3. 03

    Normalize and chunk authorized documents without losing titles, URLs, dates, permissions, or section boundaries, then embed and index versioned content.

  4. 04

    Retrieve a broad candidate set and rerank a smaller set, measuring each stage separately for language balance, false positives, missing evidence, latency, and cost.

  5. 05

    Pass only cited excerpts to generation, treat retrieved text as untrusted data, enforce access at query time, and reevaluate before any model, chunking, or corpus change.

Working standard

What good use looks like.

  • Measure retrieval and reranking separately.
  • Preserve source and permission metadata.
  • Re-embed after model changes.

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.