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Course 4

Applications with LLMs

APIs, RAG, agents and LLMs in production

  • ≈ 225 h
  • Level: intermediate
  • 4 units
  • 30 lessons

The daily work of an application AI engineer. Serve models as reliable APIs, search your documents with RAG, build agents with tools and take it all to production with evals, guardrails, cost and observability. You can start right after course 1; whatever you need about how models work inside is explained "just enough to get started".

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What you'll be able to do

  • An AI API with streaming, rate limits and Docker deployment
  • A RAG system over your documents, with hybrid search and evaluation
  • A secure agent with tools and a production support assistant with evals and traces

Prerequisites

It helps to have done first (or to master what it teaches):

Units of the course

Unit 1 · APIs and services for AI

Turning a model into a reliable service. HTTP and REST, APIs with FastAPI and Pydantic, LLM streaming, usage limits and keys, and deployment with Docker, environment configuration and health checks.

5 lessons · ≈ 13 h of study

  1. HTTP and REST — the language of services
  2. Building an API with FastAPI
  3. Serving a model or an LLM behind an API
  4. Deploying — Docker, configuration and health checks
  5. Your first real call to an LLM

See the unit: APIs and services for AI

Unit 2 · RAG

7 lessons · ≈ 25 h of study

  1. Ingestion and chunking — getting documents ready to be found
  2. Lexical retrieval — inverted index, TF-IDF and BM25
  3. Dense retrieval — embeddings, top-k and approximate indexes
  4. Hybrid search, reranking and query rewriting
  5. Building and evaluating a RAG system — prompts with citations, metrics and failure analysis
  6. Graph RAG — retrieving by following relationships
  7. Multimodal RAG — tables, images and PDF layout

See the unit: RAG

Unit 3 · AI agents

6 lessons · ≈ 21 h of study

  1. Tool calling and structured outputs
  2. The agent loop
  3. Memory and context management
  4. Workflows, orchestration and MCP
  5. Agent evaluation and security
  6. Multi-agent systems — coordinating without losing control

See the unit: AI agents

Unit 4 · Engineering LLM applications in production

The daily work of an AI engineer. Versioned prompts, structured outputs, tools, evals, guardrails, cost, observability, RAG and agents in production, and an end-to-end support assistant.

12 lessons · ≈ 40 h of study

  1. Serious prompt engineering — templates, few-shot and versions
  2. Structured outputs — JSON you can trust
  3. Function calling in depth — tools that hold up in production
  4. Evals for LLM applications — knowing whether your change improves anything
  5. Guardrails and security — layered defence
  6. Cost and latency — the bill and the clock
  7. LLMOps observability — seeing what happens inside
  8. RAG in production — freshness, permissions and citations that hold up
  9. Agents in production — budgets, humans and resumption
  10. Prompting, RAG or fine-tuning? Deciding with judgment
  11. Multimodal and voice in applications
  12. Capstone project — an end-to-end support assistant

See the unit: Engineering LLM applications in production

Course projects

  • A REST API for a model ≈ 30 h
    A real FastAPI service around a model: Pydantic v2 validation, API keys, uniform errors, cursor pagination, rate limiting, OpenAPI, tests with TestClient, health checks and a Dockerfile.
  • A RAG system ≈ 45 h
    A retrieval-augmented system with indexing, hybrid search, citations and evaluation.
  • An AI agent ≈ 45 h
    An agent that plans, uses tools and recovers from errors, with an evaluation of its reliability.

The full explanations, auto-graded exercises, exams and projects are inside the academy. Estimated hours for someone starting from scratch: lessons with practice and review, challenges, exams and the recommended projects. It's the same estimate you'll see inside the academy.

Along the way you earn yang, the academy's currency: 10 for every lesson, 50 for every unit exam you pass and 150 for every verified project, plus 15 for today's challenge and 100 for the weekly one. You spend it in the shop on mascots and colours; it can't be bought with money.

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