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AI with Python

Move past toy prompts to build production-ready AI applications: structured outputs, semantic search, RAG, and autonomous tool-using agents.

  • 12 modules
  • 12 lessons
  • Shallows to Landfall
  • Free to learn

This course is being written. Its lessons will be free here, like every lesson on Mawj. The outline below is what it will teach; follow the Telegram channel to hear the day it opens.

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What you'll be able to do

  • Call leading LLM APIs programmatically and reliably
  • Extract validated data using Pydantic structured outputs
  • Implement semantic search and retrieval-augmented generation (RAG)
  • Build autonomous tool-using AI agents that solve complex workflows

The full syllabus

12 modules, from the Shallows down. Open any module to see its lessons.

The Shallows

Modules 1–3, 3 lessons

Understanding LLMs, calling APIs from Python, and prompt engineering.

  1. , coming soon
    1How LLMs workIntuition for tokens, context windows, and probabilistic text generation.
    1. 1.1Tokens and context windowsSoon
  2. , coming soon
    2Calling LLM APIsConnect to model APIs from Python, manage keys, and stream responses.
    1. 2.1Your first LLM API callSoon
  3. , coming soon
    3Prompt engineeringInstructions, few-shot examples, system messages, and role definitions.
    1. 3.1Writing effective promptsSoon

Currents

Modules 4–7, 4 lessons

Structured outputs, data manipulation, embeddings, and RAG.

  1. , coming soon
    4Structured outputs with PydanticEnforce strict JSON schemas and parse LLM outputs into typed objects.
    1. 4.1Extracting structured dataSoon
  2. , coming soon
    5Data essentials: NumPy & pandasJust enough data handling for vectors, matrices, and tabular datasets.
    1. 5.1Working with datasetsSoon
  3. , coming soon
    6Embeddings and semantic searchConvert text into high-dimensional vectors and perform cosine similarity search.
    1. 6.1Generating text embeddingsSoon
  4. , coming soon
    7Retrieval-augmented generation (RAG)Chunk documents, retrieve relevant contexts, and synthesize grounded answers.
    1. 7.1Building a RAG pipelineSoon

Deep Water

Modules 8–10, 3 lessons

Tool calling, autonomous agents, evaluation, and production safety.

  1. , coming soon
    8Tool use and agentsLet an LLM call Python functions, search databases, and take actions.
    1. 8.1Function calling and tool useSoon
  2. , coming soon
    9Evaluating AI featuresMeasure accuracy, hallucinations, and regression across prompt revisions.
    1. 9.1Automated AI evaluationsSoon
  3. , coming soon
    10Cost, latency, and safetyRate limiting, caching, cost monitoring, and guardrails for production.
    1. 10.1Production guardrails and costSoon

Landfall

Modules 11–12, 2 lessons

Ship AI behind FastAPI and complete the document assistant capstone.

  1. , coming soon
    11Shipping AI behind FastAPIExpose streaming AI features and RAG pipelines via fast web endpoints.
    1. 11.1Streaming API endpointsSoon
  2. , coming soon
    12Capstone: Document assistantBuild an end-to-end assistant that answers questions over custom PDF and markdown files.
    1. 12.1Building the document assistant (Project)Soon

Who it's for

  • Python developers wanting to integrate modern AI into real products
  • Software engineers who want to build with RAG, embeddings, and agents
  • Students aiming for practical AI engineering skills beyond theory

What you need

  • Comfortable with Python functions, dictionaries, and virtual environments
  • An internet connection and an API key for experimentation