AI Engineering
9 stories tagged AI Engineering on Learning Fanatic.
- Lesson 3.1: Prompts, system messages and parameters How to talk to a model through an API: the system message, the user message, examples and the settings that shape the reply. With a worked prompt you can adapt.
- Lesson 3.3: Streaming, latency and cost of a request Every model request has a price and a wait. Use the sliders to see how input size, output size and volume change your bill and what the user feels.
- Lesson 8.2: Caching and batching to serve more requests Batching lets a model serve many requests at once, and caching avoids repeat work. Watch static, dynamic and continuous batching schedule the same six requests.
- Lesson 2.3: Temperature and sampling explained Temperature decides how adventurous a model's word choices are. Move the slider to see the probabilities reshape, and learn which setting fits which task.
- Lesson 1.2: The parts of an LLM application Almost every language model product has five parts: input, context, model, output handling and feedback. Learn the map so every later lesson has a place.
- Lesson 1.3: Build, buy or fine-tune a model Before training anything, try the cheapest options first. A practical ladder from prompting to retrieval to fine-tuning, and when each one is worth the cost.
- Lesson 2.1: Tokens and context windows explained Language models read tokens, not words, and can only hold so many at once. Learn what tokens are, how to estimate them, and why the context window matters.
- Lesson 2.2: How a model predicts the next token A language model works by predicting one token at a time. Understand probabilities, why answers vary, and why fluent text is not the same as true text.
- Lesson 1.1: What an AI engineer actually builds An AI engineer builds products on top of existing language models rather than training them. Learn what the job covers and how it differs from data science.