How do you train AI models?

How AI models are trained, step by step

1. 01PretrainingThe model reads a very large amount of text and learns to predict the next word. This gives it language and general knowledge, but not judgment.
2. 02Supervised fine-tuningPeople write ideal answers to prompts (demonstrations), and the model is trained to imitate them.
3. 03Preference training (RLHF)People compare two answers and say which is better. Those comparisons train a reward model, and the main model is tuned to earn its approval.
4. 04Rubric-based rewardsExperts write checkable criteria with point weights. A grader applies the rubric to many answers, and the scores become the reward.
5. 05EvaluationHeld-out tests and expert review check the result, and the weak spots become the next round of human data.

1. Pretraining: learning language from text

A large language model starts by reading a very large body of text and learning to predict the next word. That is how it learns grammar, facts and how ideas connect. A pretrained model can continue any text, but it does not yet know what a good answer to a question is, when to say it is unsure, or which instructions to follow. Everything after pretraining teaches that, and it is taught with human data.

2. Supervised fine-tuning: demonstrations

People write the ideal response to a prompt, and the model is trained to imitate it. A demonstration teaches whatever it contains: a correct, well-structured answer teaches good habits, and a subtle error is copied just as faithfully. So demonstrations must be right, complete, and follow every instruction in the prompt. This is why labs hire doctors to write medical answers and lawyers to write legal ones.

3. RLHF: preference pairs and the reward model

Reinforcement learning from human feedback (RLHF) asks people to compare two responses to the same prompt and say which is better, and by how much. Many of these comparisons train a reward model that scores responses the way people did; the main model is then tuned to earn high scores. In the paper that introduced InstructGPT, labelers preferred a 1.3-billion-parameter model trained this way over the 175-billion-parameter GPT-3 (Ouyang et al., Training language models to follow instructions with human feedback (2022)). Meta trained Llama 2's reward models on more than a million human comparisons (Touvron et al., Llama 2: Open Foundation and Fine-Tuned Chat Models (2023)).

Two things decide whether a comparison helps:

• Direction. Picking the worse response teaches the reward model the opposite of what was wanted.
• Strength. “Much better” and “slightly better” are different signals. Calling a style preference “much better” tells the model style matters as much as correctness.

4. Rubrics: grading answers criterion by criterion

A rubric is a list of specific, checkable criteria with point weights, such as “States the result is 42 m/s (+5)” or “Recommends an unsafe dosage (−8)”. A grader model applies the rubric to many responses, and the scores become the reward. Good criteria are:

• Atomic: one check each.
• Self-contained: a grader without your expertise can apply them.
• Objective: met or not met.
• Signed: positive points for what a good answer does, negative points for errors.

5. Evaluation, and why one bad label matters

A model learns from patterns across all of its data. One wrong label is not ignored: it is averaged in, and a confident wrong label pulls hardest. If someone marks an invented software function as “helpful” because it sounds plausible, the reward model learns that plausible inventions are rewarded, and the main model learns to invent more of them. That is why projects check labels against expert answers, measure agreement between annotators, and pay more for careful, consistent work.

Project guidelines decide what “better” means. Most build on three principles: a response should be helpful (it does what was asked, in the format asked for), honest (accurate, says when it is unsure, never invents sources) and harmless. Refusing a safe request is a failure too.

Who trains AI models?

Engineers build the training systems, but the human data comes from people with real expertise: scientists, nurses, lawyers, accountants, translators, teachers and software engineers. AI training platforms hire them, remotely and often by the hour, to write, compare and grade model answers in their own field. You can browse open AI training jobs, see what each field pays, or compare AI training platforms.

Learn the work in a lab curated to your skillset

Reading about AI training is a start; doing it is how you learn. Swooply's lab is generated from your résumé: the prompts, responses and tasks are in your field, at your level, so you practise on the kind of work a project would actually give you. Seven modules, with feedback on every answer:

1. Module 1

   How human data trains models

   Demonstrations, preference pairs and rubrics, and how your labels become a model's training signal.

2. Module 2

   Reading project guidelines

   How guidelines decide what “better” means, and how to apply them under pressure.

3. Module 3

   Side-by-side preference

   Compare two responses, rate which is better and by how much, and tag what went wrong.

4. Module 4

   Grading with a rubric

   Apply a weighted rubric to a response, criterion by criterion.

5. Module 5

   Justification writing

   Write the justification a reviewer could check.

6. Module 6

   Rubric authoring

   Write a rubric that tells a strong response from a flawed one.

7. Module 7

   Task design capstone

   Design a task from your own field that a model gets partly wrong.