The outcome

Answer from an approved corpus with measurable retrieval quality instead of relying on unsupported model memory.

Cohere is a enterprise language model, embedding, reranking, and retrieval platform. Building enterprise text generation and retrieval systems with Chat, multilingual embeddings, semantic Rerank, tool use, and grounded generation. 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 questions, corpus owners, access rules, freshness, languages, citation format, and a benchmark with relevant and unanswerable cases.

  2. 02

    Clean and chunk approved documents, preserve source IDs and permissions, then embed documents with the correct input type and pinned model.

  3. 03

    Embed each query with the search-query input type, retrieve a broad candidate set, and rerank candidates using stable document identifiers.

  4. 04

    Send only top verified passages to Chat with an instruction to cite sources and abstain when evidence is missing or contradictory.

  5. 05

    Measure retrieval recall, reranking quality, answer support, latency, and billed units; version the index and rerun tests on model changes.

Working standard

What good use looks like.

  • Use the correct embedding input types.
  • Evaluate retrieval before generation.
  • Preserve source IDs through reranking.

Generated text and semantic rankings can be wrong, biased, or insufficiently grounded. Protect API keys, minimize sensitive context, preserve document permissions and identity, use the correct embedding input types, validate response and citation support, evaluate multilingual and adversarial cases, and monitor model versions, rate limits, billed units, and production-key usage.

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.