AI & Generative AI · Generative AI Development
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
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?
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Software developers
You want to add generative AI features to the applications you build.
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Developers new to LLMs
You have programming experience and want a structured start with LLM development.
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Product-focused engineers
You prototype features and want them to be reliable enough to ship.
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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.
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Module 1: Generative AI and LLM basics
What generative models do and how developers work with them.
Topics covered include
Practical activity: Experiment with model settings and record how outputs change.
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Module 2: Prompt engineering
Techniques that make model behaviour more reliable.
Topics covered include
Practical activity: Improve a weak prompt until it meets a set of quality checks.
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Module 3: LLM APIs and application integration
Connect models to your application code.
Topics covered include
Practical activity: Integrate an LLM into a small sample application.
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Module 4: Structured outputs and tool calling
Get results your code can use, and let models request actions.
Topics covered include
Practical activity: Build a feature that extracts structured data and calls a sample tool.
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Module 5: Retrieval-augmented generation
Ground model responses in your own documents.
Topics covered include
Practical activity: Add document retrieval to the sample application.
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Module 6: Testing and evaluation
Check that generative AI features work, and keep working.
Topics covered include
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
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Recommended background
Programming experience is recommended. Examples typically use Python.
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Technical prerequisites
Familiarity with APIs and JSON.
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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.
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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.