Tribe AI × OpenAI Cookbook: Practical Guide to OpenAI Model Selection

As part of our collaboration with OpenAI, we co-authored a hands-on guide to help technical teams move from experimentation to production by selecting the right OpenAI models for real-world workloads.

The OpenAI Cookbook: Practical Guide for Model Selection is a comprehensive, prescriptive resource shaped by direct experience deploying enterprise-grade GenAI solutions in production. It goes beyond benchmark comparisons to offer practical frameworks, tradeoff analyses, and implementation-tested patterns that help teams make grounded, scalable decisions.

What's inside:

• A model comparison matrix covering GPT‑4.1, GPT‑4o, o3, and o4‑mini, outlining strengths, trade-offs, and use case fit
• An adaptation decision tree to narrow model selection based on workload profiles (e.g., retrieval, vision, function-calling)
• Three production-grade workflows: Legal research with agentic RAG and recursive chunking Pharma R&D using multi-agent reasoning and workflow tuning Insurance claims with GPT‑4 Vision + structured extraction

• A prototype-to-production checklist covering observability, cost, safety, and integration
• Guidance on evaluating tradeoffs between latency, cost, accuracy, and model capabilities
• Self-contained notebooks and modular code that can be adapted directly into production workflows

Whether you're working on retrieval, document understanding, multi-agent orchestration, or vision-based applications, this guide provides a practical foundation for selecting, prompting, and deploying the right OpenAI model with confidence.

Explore the full guide here.