How AI Is Trained

Nobody programs an LLM’s knowledge by hand. Here’s the pipeline that turns raw text into a capable model.

1. Data collection

Training starts with enormous datasets — books, articles, websites, code — cleaned and filtered for quality. The old rule still applies: better data beats more data.

2. Pre-training

The model reads the data and learns to predict the next word, over and over, billions of times. This is where it absorbs grammar, facts, reasoning patterns, and even some biases present in the data. Pre-training is the most expensive step, requiring thousands of specialized chips running for weeks.

3. Fine-tuning

After pre-training, the model is tuned on smaller, high-quality datasets of instructions and good responses, so it learns to follow directions and be helpful rather than just continuing text.

4. Alignment (RLHF)

Human reviewers rank the model’s answers; the model is then optimized to prefer the highly ranked ones. This step — reinforcement learning from human feedback — is what makes assistants polite, safe, and genuinely useful instead of merely fluent.

5. Evaluation

Before release, models are tested on benchmarks measuring knowledge, reasoning, coding, and safety. Training never fully ends: real-world feedback keeps improving the next version.

Try it yourself: prompting exercises

Training a model takes a data center. Training yourself to use one well takes five minutes. Try these:

  1. Be specific. Ask for “a 3-bullet summary of this article for a busy landlord” instead of “summarize this.”
  2. Give context. Paste the background first, then the question. Watch the answer get dramatically better.
  3. Set the format. Ask for a table, an email draft, or bullet points — don’t accept a wall of text you didn’t ask for.

The best way to practice is with a real assistant. Try Muse with my invite code →