Enterprise language AI
How to use
Cohere.
Building enterprise text generation and retrieval systems with Chat, multilingual embeddings, semantic Rerank, tool use, and grounded generation.
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 covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to Cohere
Get into the official Cohere experience with the right account and a setup you understand.
- 01
Start at https://cohere.com/ and confirm the domain before entering account or payment information.
- 02
Availability: Cohere is accessed through its dashboard, hosted API, SDKs, cloud-provider integrations, and private deployment options rather than a required desktop app.
- 03
A Cohere account and API key or approved cloud deployment, a secure application environment, and an evaluated model and data path.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 05
Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
First session
Your first useful Cohere session
Learn the interaction loop using a small task with a clear outcome.
- 01
Create a development key and define a small benchmark for one text generation or retrieval task.
- 02
Pin an available model and send a minimal request with explicit system instructions and output limits.
- 03
Validate the response structure, finish reason, token and billed-unit metadata, latency, and error handling.
- 04
Compare results across representative and adversarial cases before connecting private data or production traffic.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
Quality control
Check the quality of Cohere output
Establish that a retrieved answer is actually supported by the sources it cites.
- 01
Restate the question precisely; retrieval quality degrades fastest on vague or compound queries.
- 02
Open the cited sources and confirm each one genuinely contains the claim attributed to it.
- 03
Check the publication and capture dates — stale sources are the most common cause of confidently wrong answers.
- 04
Look for what is missing: retrieval returns what matches the query, not what would contradict it.
- 05
Confirm quotations are verbatim and that summaries have not reversed a qualification or a negation.
- Open the citation; do not trust that a link supports the claim beside it.
- Check dates on every retrieved source.
- Treat an absent contradiction as unverified, not as agreement.
Privacy & permissions
Use Cohere safely
Control what you index and what leaves your systems when you query it.
- 01
Classify the corpus before indexing: confidential, personal, and regulated material needs an explicit decision.
- 02
Confirm where the index is stored, who can query it, and how long content is retained.
- 03
Scope crawling and scraping to sources you are permitted to collect, and respect robots and terms of use.
- 04
Strip secrets and personal data before indexing rather than filtering them at query time.
- 05
Review query logs — the questions asked can be as sensitive as the documents indexed.
- 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.
- Follow your organisation's approved-use policy.
- Never treat fluent output as authorization to act.
Core workflows
Step-by-step ways to use Cohere for the work it does best.
Each workflow below has its own outcome, steps and review checkpoints.
Combine Embed, retrieval, Rerank, and Chat while preserving document identity, evaluation evidence, and citations.
↓ 02 WorkflowShip a controlled Cohere Chat integrationCreate structured text generation with pinned models, explicit messages, bounded output, and production observability.
↓Workflow 01
Build a grounded RAG pipeline with Cohere
Combine Embed, retrieval, Rerank, and Chat while preserving document identity, evaluation evidence, and citations.
Outcome: Answer from an approved corpus with measurable retrieval quality instead of relying on unsupported model memory.
- 01
Define the questions, corpus owners, access rules, freshness, languages, citation format, and a benchmark with relevant and unanswerable cases.
- 02
Clean and chunk approved documents, preserve source IDs and permissions, then embed documents with the correct input type and pinned model.
- 03
Embed each query with the search-query input type, retrieve a broad candidate set, and rerank candidates using stable document identifiers.
- 04
Send only top verified passages to Chat with an instruction to cite sources and abstain when evidence is missing or contradictory.
- 05
Measure retrieval recall, reranking quality, answer support, latency, and billed units; version the index and rerun tests on model changes.
What good use looks like
- Use the correct embedding input types.
- Evaluate retrieval before generation.
- Preserve source IDs through reranking.
Workflow 02
Ship a controlled Cohere Chat integration
Create structured text generation with pinned models, explicit messages, bounded output, and production observability.
Outcome: Operate a predictable Chat API workflow whose data, behavior, cost, and failures can be reviewed.
- 01
Specify the task, data classification, system rules, response schema, maximum output, latency target, budget, and refusal behavior.
- 02
Create a project-specific key in a secret store, pin an available model version, and send the system message first with minimal user context.
- 03
Validate finish reason and response structure, reject malformed output, and add timeouts, rate-limit handling, bounded retries, and idempotency.
- 04
Run factual, adversarial, multilingual, long-context, and schema tests while recording quality, token counts, billed units, and latency.
- 05
Redact logs, monitor drift and spend, rotate keys, keep a fallback, and require benchmark review before changing prompts or model versions.
What good use looks like
- Pin the model version.
- Validate finish reason and schema.
- Track billed units with quality metrics.
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 guide.