Purpose & Audience

This practical cookbook guides you through choosing, prompting, and deploying the optimal OpenAI model (from GPT-4.1, o3, and o4-mini) for your particular tasks. Rather than dense documentation, it offers hands-on decision tools and real-world examples to empower Solutions Engineers, Technical Account Managers, Partner Architects, and semi-technical users in rapidly creating effective solutions. The material emphasizes today's model strengths, industry-specific applications, and pressing business demands, providing straightforward steps from selection to live deployment. Every section includes succinct, ready-to-use code snippets adaptable to your scenarios, plus links to resources for in-depth exploration.

Note: The guidance and tests below use the most advanced models available now. Expect these benchmarks to shift over time with new scenarios and releases.

How to Use This Cookbook

Organized into clear sections, this cookbook lets you pinpoint exactly what you need fast. Each part addresses a key element of model choice, building, and rollout.

• Purpose & Audience: Overview of the guide's targets and scope.

• Model Guide: Fast-reference tool for picking the best model, with comparisons and evolution visuals tied to various use cases.

• Use Cases:

  3A. Long-Context RAG for Legal Q&A: Creating an agent-based system to query intricate legal documents.

  3B. AI Co-Scientist for Pharma R&D: Speeding up pharma research experiment planning via multi-agent setups.

  3C. Insurance Claim Processing: Automating handwritten insurance form analysis with vision and logic.

• Prototype to Production: Checklist for scaling prototypes into production.

• Adaptation Decision Tree: Flowchart for model choices based on your criteria.

• Appendices: Extras like pricing, speed stats, prompt templates, and resource links.

For rapid choices, head to Model Guide and Adaptation Decision Tree. Dive into Use Cases for hands-on builds.

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Model Guide

2.1 Model‑Intro Matrix

(Full price and utility table → Section 6.1)

2.2 Model Evolution at a Glance

OpenAI's model portfolio has advanced to meet targeted requirements across key areas. These visuals highlight the active model groups and their interconnections.

Fundamental Differences: "o-series" vs "GPT" Models

OpenAI provides two main model lines, each shining in distinct ways:

GPT Models (4o, 4.1): Built for versatile tasks with top-tier instruction adherence. GPT-4.1 leads in extended contexts (1M tokens), while GPT-4o supports real-time speech, text-to-speech, and speech-to-text variants. GPT-4.1 offers mini and nano options; GPT-4o has a mini. These smaller versions cost less and run quicker than the full models.

o-series Models (o3, o4-mini): Focused on advanced reasoning and sequential problem-solving. Ideal for intricate, phased tasks needing logic and tools. Prioritize them for top accuracy and depth. They include a reasoning_effort option (low, medium, or high) to manage reasoning token usage.

OpenAI Model Evolution

Key Characteristics

• GPT-4.1 Family: Tailored for 1M token context handling.

• o3: Expert in intensive multi-step reasoning.

• o4-mini: Merges reasoning with vision at budget-friendly rates.

Each shines in unique settings, with strengths that pair well for sophisticated pipelines.

In this cookbook, we tested only GPT-4.1 series, o3, and o4-mini. GPT-4o series were not included.

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3A. Use Case: Long-Context RAG for Legal Q&A

🗂️ TL;DR Matrix

This table captures the key tech picks and reasons for this Long-Context Agentic RAG setup.

Note: Prices and model identifiers accurate as of April 2025, subject to change.

This guide details building a Retrieval-Augmented Generation (RAG) system to deliver precise answers from extensive, complex procedural documents, using the Trademark Trial and Appeal Board Manual of Procedure (TBMP) as the example. The TBMP outlines USPTO Trademark Trial and Appeal Board procedures for trademark disputes and is a go-to for IP lawyers. Harnessing cutting-edge OpenAI models, the system boosts comprehension of heavy legal material, delivering exact, grounded replies via superior language processing and adaptive retrieval.

These methods extend to scenarios demanding exact pulls from tough docs, like medical regs, finance rules, or tech specs where precision, sourcing, and traceability matter most.

1. Scenario Snapshot

• Corpus: Core doc is the 2024 Trademark Trial and Appeal Board Manual of Procedure (TBMP), spanning 1194 pages of rules and advice.

• Users: IP litigators and paralegals seeking fast, spot-on answers to procedure queries drawn solely from the TBMP.

• Typical Asks: Queries needing blending and references, e.g.:

"What are the requirements for filing a motion to compel discovery according to the TBMP?"

• "What deadlines apply to discovery conferences as specified in the manual?"

• "Explain how the Board handles claims of attorney-client privilege during depositions according to the TBMP."

• "Enumerate the Fed. R. Civ. P. 11 sanctions the Board can invoke according to the TBMP."

Note: Tailor steps to your setup as needed.

While OpenAI's File Search works well for basics, this approach uses 1M-token windows for full docs sans prep or vector stores. The agentic method supports instant loading, flexible retrieval scales, and detailed citations.

2. Agentic RAG Flow

Before code, grasp the workflow:

• Load the entire document into the context window

• Split into 20 chunks that respect sentence boundaries

• Ask the model which chunks might contain relevant information

• Drill down into selected chunks by splitting them further

• Repeat until we reach paragraph-level content

• Generate an answer based on the selected paragraphs

• Verify the answer for factual accuracy

This tiered search echoes human skimming: scan whole, zoom to chapters, sections, then key paras.