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E-learning materials

Building AI Applications with Python & APIs

Ship a real AI application in Python: call model APIs, add retrieval and tools, manage cost and errors, and deploy it for other people to use.

Material Overview
Building AI Applications with Python & APIs

Put everything together into a working product. You will call model APIs from Python, stream responses, add function and tool calling, connect a retrieval layer over your own data, and build a simple interface around it. This E-Learning Material covers API keys and secrets, rate limits and retries, token cost control, caching, logging, evaluation before release, and the deployment options that suit a small team.

Curriculum

Learning Path

Each level is enrolled separately. Work through them in order, or start at the level that matches your experience.

  1. Level 01

    Beginner

    Purpose

    Make your first API call and turn a model response into something a program can use.

    Outcome

    You can call a model API from Python and use the result inside your own code.

    What You'll Learn
    • Set up a project, install the client, and store an API key safely
    • Send a request, read the response, and handle the common errors
    • Turn a prompt into a small script that does one useful thing

    247 CREDS

  2. Level 02

    Intermediate

    Purpose

    Build an application around the model, with structured output, streaming, and a usable interface.

    Outcome

    You can ship a small application that other people can actually run and use.

    What You'll Learn
    • Request structured responses and validate them before use
    • Stream output so the interface responds while the model is still writing
    • Build a simple web or command line interface around the workflow

    489 CREDS

  3. Level 03

    Advanced

    Purpose

    Give the application access to data and tools so it can answer from sources and take action.

    Outcome

    You can build an application that reasons over your data and performs real actions safely.

    What You'll Learn
    • Add a retrieval layer over your own documents with citations in the response
    • Implement tool calling so the model can query systems and run functions
    • Manage conversation state, context limits, and token cost per request

    752 CREDS

  4. Level 04

    Expert

    Purpose

    Run the application in production with evaluation, observability, and cost control that hold under load.

    Outcome

    You can operate an AI application in production and keep its quality and cost under control.

    What You'll Learn
    • Add logging, tracing, and evaluation that catch quality regressions early
    • Handle rate limits, retries, timeouts, and graceful degradation
    • Deploy with secrets management, caching, and a budget you actively monitor

    1,042 CREDS