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cohere-cookbooks skill

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This skill helps you leverage Cohere cookbooks to implement RAG, agents, embeddings, and enterprise patterns for production-grade AI applications.

npx playbooks add skill rshvr/unofficial-cohere-best-practices --skill cohere-cookbooks

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SKILL.md

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---
name: cohere-cookbooks
description: Official Cohere cookbooks and tutorials for production patterns. Links to RAG implementations, agent workflows, enterprise integrations, and real-world use cases from the Cohere developer experience repository.
---

# Cohere Cookbooks Reference

## Official Resources

- **Docs & Cookbooks**: https://github.com/cohere-ai/cohere-developer-experience
- **API Reference**: https://docs.cohere.com/reference/about

## Official Cookbook Repository

The [cohere-developer-experience](https://github.com/cohere-ai/cohere-developer-experience) repository contains official, up-to-date cookbooks covering:

### RAG & Search
- Basic RAG implementation
- Multi-hop RAG for complex queries
- Reranking integration patterns
- Hybrid search (keyword + semantic)

### Agents & Tool Use
- ReAct agent patterns
- Multi-tool orchestration
- LangChain/LangGraph integration
- Human-in-the-loop workflows

### Embeddings
- Semantic search implementations
- Document clustering
- Similarity-based recommendations
- Multilingual embedding use cases

### Enterprise Patterns
- Production deployment guides
- Batch processing at scale
- Error handling and retries
- Cost optimization strategies

## How to Use the Cookbooks

1. **Browse the repository**: https://github.com/cohere-ai/cohere-developer-experience
2. **Find relevant notebooks**: Look in the `notebooks/` or `cookbooks/` directories
3. **Run locally**: Clone and run Jupyter notebooks with your API key

```bash
git clone https://github.com/cohere-ai/cohere-developer-experience.git
cd cohere-developer-experience
pip install -r requirements.txt
jupyter notebook
```

## Quick Links

| Topic | Description |
|-------|-------------|
| RAG | Retrieval-augmented generation patterns |
| Agents | Tool use and agentic workflows |
| Embeddings | Vector search and semantic similarity |
| Reranking | Two-stage retrieval optimization |
| Streaming | Real-time response generation |
| Structured Output | JSON mode and schema enforcement |

> **Note**: The official cookbooks are continuously updated. Always refer to the GitHub repository for the latest patterns and best practices.

Overview

This skill packages official Cohere cookbooks and tutorials into a practical reference for production AI patterns. It summarizes RAG, agents, embeddings, reranking, streaming, and structured-output approaches so teams can adopt proven implementations quickly. The content focuses on deployment-ready patterns, error handling, and cost optimization for Cohere APIs.

How this skill works

The skill inspects canonical cookbook examples and tutorials covering retrieval-augmented generation, multi-tool agents, embedding pipelines, and reranking workflows. It highlights concrete code patterns, architecture options, and operational guidance for integrating Cohere chat, embeddings, and streaming APIs. Recommendations emphasize real-world constraints like latency, cost, and reliability.

When to use it

When building a RAG system for knowledge-grounded responses
When orchestrating agents with tool use or human-in-the-loop steps
When implementing semantic search, clustering, or recommendation features
When you need streaming responses or structured JSON outputs from models
When preparing production deployments with scaling and cost controls

Best practices

Start with small prototypes using example notebooks, then productionize incrementally
Use hybrid search and two-stage retrieval (search + rerank) for high-precision results
Enforce schema or JSON mode for predictable structured outputs and downstream parsing
Instrument latency, cost, and error metrics; add retries and backoff for external calls
Design agents with clear tool interfaces and guardrails to limit hallucinations

Example use cases

Customer support assistant that retrieves product docs and composes answers via RAG
Multilingual semantic search across knowledge bases using embeddings and clustering
Agent that calls internal APIs, performs computations, and returns structured JSON
Batch embedding pipeline for large document collections with cost-optimized indexing
Live transcript summarization with streaming model responses and progressive output

FAQ

Are these cookbooks production-ready?

They present production patterns and working examples, but each integration should be validated, secured, and tested with your data and traffic profile before full production rollout.

Which pattern improves search precision most?

A two-stage pipeline—initial semantic or keyword retrieval followed by reranking—typically yields the best precision for complex queries.