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Integrate Large Language Models into Real Applications

Learn how to build applications with large language models, from effective prompts and API integration to structured outputs, tool calling, retrieval and testing.

  • Prompt engineering
  • LLM APIs and application integration
  • Structured outputs and tool calling
  • Testing and evaluating AI features
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Course overview

About This Course

Generative AI Development focuses on the practical work of building features with large language models (LLMs): writing reliable prompts, calling model APIs, getting structured results and connecting models to data and tools.

Generative AI is now part of many products and internal tools. Developers who can integrate LLMs reliably, control their output and test their behaviour can turn promising prototypes into useful features.

You will learn prompt engineering techniques, how to work with LLM APIs, how to request structured output and let models call tools, how to add retrieval for grounded answers, and how to test and evaluate generative AI features.

This course sits within our AI & Generative AI learning paths. AI Engineer gives broader foundations, AI Agents & Automation builds on tool calling, and AI Security covers protecting LLM applications.

Who it is for

Who Should Take This Course?

  • Software developers

    You want to add generative AI features to the applications you build.

  • Developers new to LLMs

    You have programming experience and want a structured start with LLM development.

  • Product-focused engineers

    You prototype features and want them to be reliable enough to ship.

  • Technical leads

    You want to understand what it takes to build and test LLM features well.

Learning outcomes

What You Will Learn

By working through the course, you will build towards being able to:

  • Write clear, structured prompts and improve them systematically.

  • Call LLM APIs and handle parameters, errors, latency and cost.

  • Request structured output such as JSON and validate it in code.

  • Use tool calling to let a model request actions or data from your application.

  • Add retrieval to ground responses in your own content.

  • Design tests and evaluations that check the quality of generative AI features.

  • Recognise common failure modes and design features that handle them gracefully.

Curriculum

Course Curriculum

The course is organised into modules. Session order and depth may vary by training arrangement.

  1. Generative AI and LLM basics

    What generative models do and how developers work with them.

    Topics covered include

    • How LLMs produce text
    • Tokens, context and parameters
    • Choosing a model
    • Limitations and failure modes

    Practical activity: Experiment with model settings and record how outputs change.

  2. Prompt engineering

    Techniques that make model behaviour more reliable.

    Topics covered include

    • Instructions, context and examples
    • System prompts and roles
    • Breaking tasks into steps
    • Iterating on prompts

    Practical activity: Improve a weak prompt until it meets a set of quality checks.

  3. LLM APIs and application integration

    Connect models to your application code.

    Topics covered include

    • API requests and responses
    • Streaming output
    • Error handling and retries
    • Managing latency and cost

    Practical activity: Integrate an LLM into a small sample application.

  4. Structured outputs and tool calling

    Get results your code can use, and let models request actions.

    Topics covered include

    • JSON and schema-based output
    • Validating model output
    • Defining tools
    • Handling tool results

    Practical activity: Build a feature that extracts structured data and calls a sample tool.

  5. Retrieval-augmented generation

    Ground model responses in your own documents.

    Topics covered include

    • Embeddings and search
    • Building context from documents
    • Answer grounding and citations
    • Keeping content up to date

    Practical activity: Add document retrieval to the sample application.

  6. Testing and evaluation

    Check that generative AI features work, and keep working.

    Topics covered include

    • Test cases for AI features
    • Automated evaluation methods
    • Regression testing prompts
    • Monitoring output quality

    Practical activity: Build an evaluation suite and use it to compare prompt versions.

Practical learning

Hands-On Learning

The course is built around developing a working sample application. Examples of activities include:

Some activities use AI model APIs. Whether accounts or API access are provided, or you use your own, will be explained when you enquire.

  • Iterating on prompts against quality checks
  • Integrating an LLM API into an application
  • Building structured-output and tool-calling features
  • Writing evaluation tests for AI behaviour

Before you start

Prerequisites and Learning Requirements

  • Recommended background

    Programming experience is recommended. Examples typically use Python.

  • Technical prerequisites

    Familiarity with APIs and JSON.

  • Software and tools

    A computer with a reliable internet connection for live online sessions. Python and a code editor are used for practice. Any additional software or accounts for practical activities will be confirmed when you enquire.

  • Prior experience

    Not intended as a first programming course.

Learning Format

IT Champion Hub offers live, instructor-led online training and workshops. Formats, duration and schedules vary by course and are not the same for every program.

  • Live online sessions led by an instructor
  • Practical activities alongside explanations
  • Upcoming batches and schedules shared on enquiry

Enquire to find out the formats and upcoming batches available for this course. Enquire about this course

FAQ

Frequently Asked Questions

Do I need machine-learning experience?

No. The course focuses on building applications with existing models. You should be comfortable programming.

Which models or providers are used?

The techniques apply across major LLM providers. Ask us which models and APIs are used in your training arrangement.

Will I need access to AI model APIs?

Practical activities use AI model APIs. We'll explain how access is arranged, and any costs involved, when you enquire.

What should I take next?

AI Agents & Automation builds on tool calling to create multi-step workflows. AI Security covers how to protect the applications you build.

How can I enquire about upcoming training?

Select Enquire About This Course. The enquiry form will note that you are asking about Generative AI Development, and we'll get back to you with upcoming batches and available schedules.

Ready to Explore This Learning Path?

Have questions about the curriculum, prerequisites, or training arrangements? Get in touch with IT Champion Hub to discuss your learning goals.

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Ask about Generative AI Development on WhatsApp