How to Fine-Tune a GPT model скачать в хорошем качестве

How to Fine-Tune a GPT model

This video is the final step in moving from a prompt engineer to a true AI Automation Architect. If you’ve hit a ceiling with complex system prompts that are slow, expensive, and inconsistent, it's time to build a custom brain for your business. In this technical breakdown, I show you how to leverage OpenAI Fine-Tuning to bake your specific logic, brand voice, and formatting rules directly into a GPT model. We’ll walk through the entire lifecycle of a fine-tuned model: from preparing your JSONL training data to deploying the finalized model inside a Make.com automation. 🧠 What You’ll Learn: The "Why" of Fine-Tuning: How to achieve lower latency, massive token savings, and near-perfect accuracy. Fine-Tuning vs. RAG: When you should train a model and when you should use Retrieval-Augmented Generation (and why most people get this wrong). Data Preparation: A deep dive into structuring your .jsonl files with System, User, and Assistant roles. The OpenAI Dashboard: How to navigate the training process, monitor your fine-tuning job, and interpret the results. Make.com Integration: How to swap your base models for your new custom-trained model to build smarter, faster workflows. Whether you're looking to consistently output strict JSON or lock in a nuanced brand personality, this is the end-to-end blueprint for specialized AI. 📍 Timestamps: 0:00 - Introduction: Breaking the Prompt Engineering Ceiling 0:35 - What is Fine-Tuning? (The Trade School Analogy) 2:06 - When to Fine-Tune: Token Savings, Style & Consistency 3:15 - When NOT to Fine-Tune: Facts vs. Format (Fine-Tuning vs. RAG) 3:54 - Real-World Example: Building a Lead Qualification Model 4:39 - Preparing the Data: How to Build a JSONL File in VS Code 6:48 - Launching the Fine-Tuning Job in the OpenAI Dashboard 8:34 - Monitoring the Job: Queues, Server Load, and Epochs 9:26 - Testing Your Model: Using the OpenAI Playground for Comparison 10:10 - Token Cost Breakdown: Is Fine-Tuning Actually Expensive? 11:11 - Deploying Your Custom Model in Make.com 12:11 - Outro 🔗 Resources & Tools: OpenAI Developer Dashboard: https://platform.openai.com/ Make.com: https://www.make.com/en/register?pc=c... VS Code (for JSONL preparation): https://code.visualstudio.com/ 💡 About the Channel: I’m Christian, an AI Automation Architect. I focus on building high-level technical blueprints that bridge the gap between AI and actual business results. If you want to scale your operations using Make.com and custom AI models, hit the subscribe button. Have a specific fine-tuning use case you’re struggling with? Drop a comment below and I’ll jump in to help you architect the solution! #OpenAI #FineTuning #GPT4o #AIAutomation #MakeDotCom #AIArchitect #CustomAI #PromptEngineering #WorkflowAutomation #MachineLearning2026