A Non-Technical Guide to How AI Models Are Trained
You don't need to write code to understand the basic stages every major AI model goes through before it reaches your screen. Here's the process in plain language.
Training a modern AI model generally moves through three stages: pretraining on massive amounts of general data, fine-tuning to follow instructions and align with human preferences, and ongoing evaluation and safety testing before release. Each stage requires enormous, specialised computing infrastructure — which is why AI chips have become such a strategically important resource.
Stage one: pretraining on massive data
Every large AI model starts with pretraining: feeding it enormous quantities of text, code, or other data and having it repeatedly predict missing or upcoming pieces of that data, adjusting its internal parameters a tiny amount closer to correct each time. This stage is by far the most computationally expensive part of building a model, often requiring specialised AI chips running continuously for weeks or months across enormous data centre facilities — which is exactly why access to cutting-edge hardware has become such a central, closely watched part of the competition between AI labs.
Stage two: fine-tuning and human feedback
A freshly pretrained model is good at continuing text plausibly, but it isn’t yet good at being a helpful assistant — it doesn’t inherently know to follow instructions, stay on topic, or avoid unhelpful responses. Fine-tuning stages address this, often using techniques where human reviewers rate different possible responses and the model is further adjusted to produce outputs more like the ones people rated highly. This is the stage that turns a raw text-prediction engine into something that behaves like a helpful, instruction-following assistant.
Stage three: evaluation and safety testing
Before release, models typically go through extensive evaluation: testing on benchmark tasks to measure capability, and separately, safety testing designed to find ways the model could produce harmful, biased or dangerous outputs so those issues can be addressed before the public ever sees them. This stage has grown considerably more rigorous industry-wide as models have become more capable and as public and regulatory scrutiny of AI safety has intensified.
Why compute is the bottleneck
Every stage of this process is bottlenecked by computing power, which explains a huge amount of AI industry news that might otherwise seem unrelated: why new chip platforms generate so much attention, why AI labs sign multibillion-dollar compute deals with hardware providers, and why countries increasingly treat domestic AI compute capacity as a strategic economic priority rather than a purely commercial matter. Better algorithms matter too, but at the frontier, raw computing capacity remains one of the single biggest levers on what a new model generation can do.
What happens after release
Training doesn’t fully stop once a model ships. Many providers continue refining models based on real-world usage patterns, issue periodic updates, and maintain ongoing safety monitoring for issues that only become visible at a much larger scale of real usage than any pre-release testing process can fully replicate. Understanding a model as a continuously maintained system, rather than a finished product frozen at release, is a more accurate way to think about how these tools actually evolve over time.