beginner5 h12 lessons

AI Engineering Foundations

What a language model actually is, and what changes when you stop chatting with one and start building on it. Vendor-neutral: tokens, context, sampling, prompts, system instructions, hallucination, and the first evaluation you should write. No prior AI experience assumed.

by DevFox

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What you will cover

  1. What a Language Model Actually DoesNext-token prediction, and how that one mechanism explains almost every behaviour you will meet: fluency without understanding, confidence when wrong, and why the same question can get two different answers.25 min
  2. Tokens, Context, and Why They Cost YouHow text becomes tokens, what a context window really limits, and why input and output are priced and paced differently. Counting tokens before they surprise you, and what happens when you run out of room.25 min
  3. Sampling, Temperature, and Non-DeterminismWhy a model that is deterministic underneath gives you different answers anyway: temperature, top-p, seeds, and how to decide when variation is a feature and when it is a bug you have to design around.25 min
  4. Writing a Prompt That Holds UpThe four parts of a prompt that survives contact with real inputs — instruction, context, examples, output contract — and the vague, overloaded and politely-worded prompts that quietly fail.25 min
  5. System Prompts and Rules FilesThe durable instruction layer that sits above every request: what belongs in it, what does not, and why project rules files have become a standard part of working with AI tools regardless of vendor.25 min
  6. Few-Shot Examples That TeachWhen showing beats telling, how to choose examples that generalise instead of ones the model copies verbatim, and how a badly-chosen example set narrows the model instead of guiding it.25 min
  7. Why Models HallucinateThe mechanism behind confident invention, why asking a model to be accurate does not make it accurate, and the three things that genuinely reduce it: grounding, permission to refuse, and verifiable citations.25 min
  8. Giving the Model Your DataThe decision map every AI feature runs into: put it in the prompt, retrieve it at query time, give the model a tool to fetch it, or train it in. What each option costs and which problems it actually solves.25 min
  9. Structured Output Instead of ProseWhy parsing a model's paragraphs is a bug factory, and how asking for a schema instead turns an AI call into an ordinary function you can type, validate and test.25 min
  10. What to Never Hand a ModelSecrets, personal data, and irreversible actions: the categories that need a boundary rather than a careful prompt, plus what retention and training policies mean for data you send to a provider.25 min
  11. Measuring Instead of VibingYour first evaluation: a fixed set of real inputs, an expected outcome for each, and a number you can compare. How to build one in an afternoon and why prompt tuning without it is guessing.25 min
  12. The Shape of an AI FeatureAssembling everything so far into one small end-to-end feature — input handling, prompt, schema, validation, fallback and evaluation — and seeing which parts are AI and which are ordinary software.25 min