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.
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