The GPT Era: From GPT-1 to GPT-5 - A Complete History
Kishore Gunnam
Developer & Writer
Complete Guide to LLMs · Part 3 of 8
If transformers were the engine, GPT was the car that showed the world what it could do.
Seven years. From a modest experiment to systems millions use daily.
The big story of GPT isn’t “a model got bigger.” It’s three separate shifts happening together:
- Pre-training made models broadly capable (learn general patterns from lots of text).
- Instruction tuning / RLHF made them usable in conversation (follow the user, not just autocomplete).
- Product + tools made them practical (search, retrieval, function calling, memory-like UX).
The Scaling Journey
GPT Parameter Growth
That's roughly 10,000x growth in seven years.
GPT-1 (June 2018)
Modest by today's standards, but it proved the concept using the transformer architecture.
GPT-2 (February 2019)
OpenAI released it gradually over 9 months. Looking back, its output seems quaint. But it started important safety conversations.
GPT-3 (June 2020)
No more fine-tuning for each task - just write the right prompt. The prompt engineering era began.
InstructGPT & RLHF (2022)
Raw GPT-3 predicted text. It didn't try to be helpful. RLHF changed that.
RLHF: Teaching AI to Help
Collect Examples
Humans write ideal responses
Train Reward Model
Learn what humans prefer
Optimize with RL
Train to maximize reward
Result
Model follows instructions
This transformed GPT from "text predictor" to "helpful assistant." Full explanation in Part 6: AI Alignment.
ChatGPT (November 2022)
The model wasn't new. The interface was. Suddenly everyone could talk to AI.
GPT-4 (March 2023)
GPT-4 Turbo & 4o (2023-2024)
o1: Reasoning Era (September 2024)
GPT-5 (August 2025)
Key Lessons
Scaling works. Each 10x jump brought new capabilities.
RLHF was crucial. Turned text predictor into helpful assistant.
Interfaces matter. GPT-3 existed for 2 years before ChatGPT made it viral.
Reasoning is next. o1 showed how models think matters as much as size.
Common beginner mistakes
- Thinking GPT models “know the truth.” They generate likely text unless grounded with sources.
- Thinking “more parameters” automatically means “better.” Training data, tuning, and evaluation matter as much.
- Mixing up “ChatGPT features” (tools, browsing, memory) with “model capability.”
What's Next?
In Part 4, we explore the competition - Claude, Gemini, LLaMA, and how the landscape shaped 2025.
Complete Guide to LLMs · Part 3 of 8