Trail 01 · 14 lessons · 14 on video
LLM foundations for engineers
Start here. What changes when a model becomes part of your system: prompts as interfaces, structured output, tools, and when a fixed workflow beats an agent.
- 01Fundamental▶ 8 minHow to read this catalogThe five pillars, how pages connect, and where to start for your role.
- 02Fundamental▶ 9 minLLMs for engineersThe model as a probabilistic function: what goes in, what comes out, and what it never promises.
- 03Fundamental▶ 8 minTokens and the context windowWhy everything costs tokens, why the middle of the context fades, and how to measure it.
- 04Fundamental▶ 8 minSampling and temperatureWhere answer variation comes from and when you want more or less of it.
- 05Fundamental▶ 8 minThe prompt as an interfaceTreat the prompt like an API contract: input, output, version, and test.
- 06Smell▶ 8 minPrompt BloatThe system prompt is past 3k tokens and every fixed bug became one more paragraph.
- 07Pattern▶ 8 minStructured OutputRequire the model to answer in a schema and validate the response.
- 08Fundamental▶ 8 minTools and function callingHow the model asks your code to act and who decides what happens next.
- 09Pattern▶ 8 minTool UseGive the model well described functions to fetch data and take action.
- 10Fundamental▶ 8 minChains, agents, and workflowsThe difference between code choosing the path and the model choosing the path.
- 11Pattern▶ 9 minPrompt ChainingSplit the task into fixed steps where each output feeds the next input.
- 12Pattern▶ 9 minRoutingClassify the input and send each kind down a specialized path.
- 13Smell▶ 8 minAgent Where a Chain Would DoThe path never changes, yet an autonomous agent decides the order on every run.
- 14Real-world scenario▶ 10 minSupport bot with human escalationA WhatsApp bot that answers questions, looks up orders, and knows when to call a human.
- Final projectLangGraph LabStore support on WhatsAppA bot that handles orders, billing and exchanges, and knows when to call a person.