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AI · Course 06 · 80 hours · 7 modules

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Generative AI & LLM Engineering

Build AI features that hold up in production.

A practical course on language models, retrieval, and agent workflows, focused on structured outputs, observability, and cost—not demos.

PythonFastAPILangChainLangGraphAzure AI FoundryFAISSQdrantLangSmithLangfuse
Who it's for
Developers and engineering teams adding LLMs and agents to real products
Duration
80 hours
Modules
7 modules, ending in a capstone project
Formats
Corporate training · College & university sessions · Own institute & online courses · Mentoring & internships

Syllabus

What the course covers

01

LLM fundamentals

  • Tokens, context windows, and model selection
  • Calling model APIs, including Azure AI Foundry
  • Prompt design patterns
02

Structured outputs and tool calling

  • Schema-constrained responses
  • Retries, validation, and error handling
  • Connecting APIs as tools
03

Retrieval-augmented generation

  • Embeddings and chunking
  • Vector search with FAISS and Qdrant
  • Hybrid ranking with BM25
04

Agents with LangChain and LangGraph

  • Stateful multi-step workflows
  • Memory and checkpointing
  • Multi-step execution and retries
05

Serving AI with FastAPI

  • Designing AI-backed APIs
  • API performance and response latency
  • Persisting conversations and agent state
06

Evaluation and observability

  • Tracing and evaluation with LangSmith
  • Prompt and cost monitoring with Langfuse
  • Token usage and latency tuning
07

Capstone project

  • Design and ship a tool-connected agent
  • Evaluate it against a test set before release

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Testimonials

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