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Building AI Apps with LLMs

From first API call to a feature you can ship: prompting, structured output, tool calling, streaming, and cost control.

  • Intermediate
  • 2h 27m
  • 4 modules
  • 12 lessons
  • 26.4k students
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Taught by Priya Raman · LLMs, RAG, Prompt engineering, Evaluation

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What you’ll learn

  • Understand the model

    Reason about tokens, context windows, and sampling instead of treating the model as a black box.

  • Get structured output

    Make a model return data your program can rely on, and validate it before you use it.

  • Wire up tool calling

    Let the model call your functions, and keep the loop bounded and observable.

  • Ship it affordably

    Stream responses, cache what repeats, and keep latency and spend under control.

Course content

4 modules · 2h 27m

  1. 01LLM FundamentalsWhat the model is doing, what it costs, and which knobs actually change the output.27m
    1. 1.1Tokens and context windowsFree12m
    2. 1.2Temperature and sampling8m
    3. 1.3Choosing a model for the job7m
  2. 02Prompting That Holds UpWriting prompts that behave the same way on the thousandth call as they did on the first.35m
    1. 2.1Designing the system prompt21m
    2. 2.2Few-shot examples5m
    3. 2.3Structured output and JSON9m
  3. 03Tool Calling and AgentsLetting the model act: exposing functions, running the loop, and keeping the whole thing bounded.54m
    1. 3.1Tool calling fundamentals31m
    2. 3.2Building an agent loop16m
    3. 3.3Guardrails and evaluation7m
  4. 04Shipping to ProductionThe engineering around the model: streaming, cost, caching, and behaving well under load.31m
    1. 4.1Streaming responses to the browser9m
    2. 4.2Managing cost and latency13m
    3. 4.3Caching and rate limits9m
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