What's the actual process for building a custom GPT with my own data?

You don't 'train' a custom GPT like you might think. You give an existing GPT temporary access to your documents so it can look up answers on the fly. This is called Retrieval-Augmented Generation, or RAG. It's a mouthful, but the idea is simple. The AI isn't learning your data by heart. It's more like giving it a library card to a private collection of your files. When you ask a question, it first runs a quick search through your stuff, finds the relevant bits, and then reads those bits to craft an answer. It's a read-and-respond process, not a memorization one. This is way cheaper and faster than actual training. Here's a concrete example. Imagine you have a folder full of old company policy PDFs. You want a bot that can answer employee questions about vacation days. You'd first chop those PDFs into small, searchable chunks—maybe a paragraph each. You store those chunks in a special database, a vector database, that finds things based on meaning, not just keywords. When someone asks, "Can I carry over my PTO days?", the system searches your chunked-up PDFs for sentences about "PTO" and "carry over." It grabs the top 3 most relevant chunks and stuffs them into the prompt for the AI, along with the original question. The AI then reads those chunks and answers, "Yes, according to the 2024 handbook, you can carry over up to 40 hours." A tip I've learned the hard way: the quality of your answer is 90% about how you chop up your documents. If your chunks are too big, the search brings back a wall of text and the AI gets lost. Too small, and you lose context. Finding the right chunk size is the real secret nobody talks about. It's the unglamorous work that makes the magic happen. Tools like OpenAI's Assistants API handle a lot of this file-chunking and searching for you, but understanding the RAG process underneath helps you troubleshoot when the bot gives a weird answer.