OpenAI training, 1-on-1.Master the API, build with GPT.
An OpenAI expert opens your project with you and fixes what matters: picking the right GPT model, making your API calls, wiring up function calling and structured outputs, building an Assistant or Agent, and shipping an app. We mean the platform for builders, not the consumer ChatGPT app. We start from your real project, not theory.
★★★★★ 4.7/5 · 300+ pros trained · France Num certified
ActiveCampaign
Adalo
AdCreative.ai
Ahref
Airtable
Allo (The Mobile First Company)
Anthropic
Apify
Apollo.io
AttioAttio Implementation Partner
Base44
Baserow
Brevo
Bright Data
Browse AI
Bubble
CaptainData
ChatGPT
Claude
Claude CodeClaude CoworkClaude Design
Clay
Clickup
Cursor
DeepSeek
Dust
ElevenLabs
Fillout
Flutterflow
Folk CRM
Folk Implementation Partner
Freepik Spaces
Gamma
GeminiWe build on the OpenAI API for clients, not just in theory.
Most OpenAI trainings are recorded tutorials by people who opened the API the night before. At Hack'celeration it's the opposite: wiring up function calling, shipping structured outputs, building Agents with tools, putting GPT apps into production for clients, that's our daily agency work. Everything we teach you, we practice on shipped projects. We know the traps (the agent that loops and burns tokens, the key exposed client-side) because we've already solved them.
- We build on the OpenAI API for clients every week, not just in theory
- 1-on-1 format: the trainer adapts to your level, beginner or seasoned dev
- We tell you when another model (Claude, open-source) earns its place for your case
- We start from your real app idea and your code, not a dummy example
Four pillars to build with GPT on the OpenAI platform.
A badly used OpenAI means the wrong model on every task, brittle parsing instead of structured outputs, an agent that loops, and a token bill that drifts. Most of the trouble comes from implementation choices, not the model. We pick up your real project and work through the four pillars together.
- The GPT models
The right GPT model, and when to pick each one
OpenAI ships a whole range: GPT-4o for multimodal and heavy reasoning, the mini versions for fast, low-cost tasks, the reasoning models for multi-step logic. We map each model to a real use so you stop defaulting to the biggest one. We also cover context, temperature, and the concrete difference with the consumer ChatGPT app, because here we're talking about the platform for builders.
Pick my model - The API and function calling
The API that calls your functions and returns clean JSON
This is the heart of the training. We set up your keys, structure your first calls to the OpenAI API, then dig into function calling: the model decides when to call one of your functions and returns typed arguments. We add structured outputs so you get valid JSON every time, no more brittle parsing. You wire this into your existing code or into Make and n8n, without rewriting your app.
See the API and function calling - Assistants and Agents
Assistants and Agents that actually use tools
We build an Assistant or Agent that keeps context, uses tools (file search, code interpreter, your own functions) and chains several steps on its own. We handle threads, memory, and the point where the agent should stop and hand control back to you. You leave with an agent that does real work on your cases, not a demo that works half the time.
See the Agents - Apps, cost and safety
Ship an app, keep cost and safety under control
We move from sandbox to production: an app or automation that runs for real. We handle response streaming, errors and rate limits, then tackle cost (tokens, caching, routing simple tasks to a mini model). On safety, we keep your keys server-side, filter inputs, and set guardrails so you don't leak data. Honest take: watching the token bill stays ongoing work, not a one-shot setting.
Plan my app
Meet our trainers, leave with a plan.
Drop your email. We get back to you to connect you with a Hack'celeration-certified trainer: we look at your app idea, spot the right GPT model and the right building block (simple call, function calling, Agent), and tell you where to start. No commitment, even if you don't take the training.
- A diagnosis of your app idea and your level
- The right model and building block on OpenAI for your case
- The right 1-on-1 format for your level and stack
- An honest take: the OpenAI API, another model, or just the ChatGPT app
Your OpenAI program, step by step.
Five steps, no skipping. Each one on your real project, with a clear deliverable. From the first session we frame your app idea and the right GPT model. By the end, you build on OpenAI without us.
- Step 1 · Audit your app idea
We frame what you want to build with GPT
First session, we look at what you actually want to do: a chatbot, an automation, an agent that processes documents, a feature inside your product. We clarify the real need, pick the right GPT model for the task, and decide between a simple API call, function calling, or a full Agent. We also flag whether the consumer ChatGPT app would be enough, because sometimes it is. You leave with a clear build plan, in priority order. No theory, your real project.
- Step 2 · Your first API calls
We make your first calls to the OpenAI API together
We get hands-on with the API. We set up your key, structure your first calls (messages, system roles, temperature, context), and read the responses together. We handle streaming to show the answer as it comes, and the basic errors (rate limits, badly scoped key, timeout). You practice on your own case, not a generic example. By the end of the step, you have a call that works from your code or from Make and n8n.
- Step 3 · Function calling and Agents
Function calling, structured outputs, then Agents
This is where GPT stops just answering and starts acting. We wire up function calling: the model decides when to call one of your functions and returns typed arguments. We add structured outputs so you get valid JSON every time. Then we build an Assistant or Agent that keeps context, uses tools and chains several steps. We handle threads and the point where the agent hands control back. You finish with an agent that does real work on your cases.
- Step 4 · Production, cost and safety
We ship your app and keep the bill under control
We go to production. We deploy your app or automation, keep your keys server-side, filter inputs, and set guardrails so you don't leak data. Then we tackle cost: we read your token consumption, set up caching and routing of simple tasks to a cheaper mini model. We're straight with you: watching the bill stays ongoing work. You leave with an app that runs and a cost you understand.
- Step 5 · Autonomy
You build on OpenAI without us
The number one goal: you become autonomous. By the end of the program, you know how to pick the right model, make your API calls, wire up function calling and structured outputs, build an Agent and ship an app with cost under control. You no longer need an agency to build with GPT. And if you want to delegate a bigger project later, we also run an OpenAI agency, but that's not the point here.
Why train 1-on-1 with us.
- 300+Pros already trained on AI
More than 300 people have gone through our trainings across France and Europe. Devs, founders, product and ops teams. Not vanity numbers: people who made their first calls to the OpenAI API and shipped a real app, instead of staying stuck at the tutorial stage.
- 4.7/5Rating across 334 verified reviews
Average rating of 4.7 out of 5, across 334 reviews. We won't pretend it's all easy: function calling and managing token cost take practice on your real project. But the 1-on-1 format makes the difference on a platform as broad as OpenAI's.
- 1:1A dedicated expert, not a class of 100
You're not a number in a webinar. A trainer opens your real project, looks at your code and your app idea, and works through your actual cases. We schedule sessions around your availability, replays included.
A working agency, recognized by the French State.
Hack'celeration is certified Activateur France Numérique and holds the AI Ambassador title, both granted by France Num to organizations that genuinely drive the digital transformation of companies. On the ground, we build on the OpenAI API for clients every week: more than 300 pros trained and a 4.7/5 rating across 334 verified reviews, left by the people who took our programs, not just by the buyer.
- Certified Activateur France Numérique
- AI Ambassador (France Num)
- 300+ pros trained across France and Europe
- 4.7/5 across 334 verified reviews
The questions we get the most.
What is a 1-on-1 OpenAI training?
An individual program with an expert on the OpenAI platform, not a class of 100 people. We open your real project, look at your code and your app idea, and work through your actual cases: picking the right GPT model, making your API calls, wiring up function calling, building an Assistant or Agent, shipping an app. You ask your questions live, the trainer adapts the pace to your level. We schedule sessions around your availability, and you leave with concrete actions every time. That's the difference between watching a tutorial and actually building on OpenAI.How much does the OpenAI training cost?
There is no single price. We connect you with a trainer certified by Hack'celeration, matched to your need and your level. It varies from one trainer to another, based on their profile and the format that fits your project.OpenAI API vs the ChatGPT app: what's the difference?
Two distinct things. The ChatGPT app is the consumer product: you chat in an interface, no code. The OpenAI API is the platform for builders: you call the GPT models from your own code or no-code tools, do function calling, build Agents, embed GPT in your app or automation. This training targets the platform and the API, not consumer chat use. If you just want to prompt ChatGPT better day to day, we tell you honestly and point you elsewhere.Do I need to code to take the OpenAI training?
Not for everything. You can wire the OpenAI API into no-code tools like Make or n8n and build automations without writing code, and we can start there. To go further (fine-grained function calling, Agents with custom tools, going to production), some technical comfort really helps, but the 1-on-1 format starts from your exact level: beginner, we go step by step; seasoned dev, we jump straight to function calling, Agents and cost optimization. You learn exactly the layer you need.What are function calling and structured outputs?
Function calling is what turns GPT from a model that talks into a model that acts: you describe your functions, and the model decides when to call them and returns typed arguments. You run the function on your side (an API call, a database query) and send the result back. Structured outputs guarantee the response matches a JSON schema you define, so no more brittle parsing on free text. It's one of the big parts of the training: we wire them into your real cases so GPT actually drives your app.What are OpenAI Assistants and Agents?
They're the building blocks to get GPT to do multi-step work, not just one answer. An Assistant keeps the context of a conversation (threads), uses tools like file search or code interpreter, and calls your own functions. An Agent goes further: it chains actions autonomously to reach a goal. In training, we build one on your real case, handle memory and tools, and above all frame the point where the agent should stop and hand control back, because an agent looping in circles burns tokens for nothing.How much does the OpenAI API cost and how do I manage tokens?
Price is counted in tokens (roughly, chunks of words) in and out, and it varies by model: the mini versions cost a fraction of a big GPT-4o. The real bill depends on your usage: an Agent that chains steps or a long context consume far more than a simple call. In training, we set up concrete guardrails (caching, leaner prompts, routing simple tasks to a mini model, tracking usage) so you feel the savings in practice. We're straight: watching cost stays ongoing work, not a one-shot setting.Is my data safe with the OpenAI API?
Fair question. On the OpenAI API and enterprise plans, data sent through the API is, by default, not used to train the models, which isn't the same policy as the consumer app. But safety also depends on your implementation: keeping your keys server-side, filtering what you send, not dumping needless sensitive data into prompts. In training, we set the best practices (protected keys, guardrails on inputs and outputs) and look at your real compliance constraints, not the marketing version.OpenAI or Claude: which one to build with?
It depends on the task. The OpenAI ecosystem is very complete for builders: mature function calling, Assistants and Agents, multimodal, wide adoption and tooling around it. Claude holds its own, and often shines on certain fine writing and very long context. The right move is rarely a single model for everything. In training we build on OpenAI, but we're honest: if your specific case is better served by another model, we say so, and show you how to keep your code flexible enough to switch.Is the training online or in person?
100% online, over video, 1-on-1. You join the sessions from anywhere, we share your screen and your real code live. Sessions are recorded if you want to revisit them. The individual format means real interaction: you're not a number in a webinar of 100, the trainer answers your questions about your project and your API. That's what makes learning concrete on a platform that moves as fast as OpenAI's.
Your app idea deserves to exist. Meet your trainer.
Drop your email. An expert who builds on the OpenAI API daily looks at your project and shows you how to go from idea to a GPT app that runs. No commitment, even if you don't take the training.