AI-assisted development landscape
- Chat assistants, IDE copilots and code-review tools.
- Where AI helps most in the development lifecycle.
- Limitations, hallucinations and outdated suggestions.
- Setting team rules for AI-generated code.
AI for developers & IT
Use AI as a disciplined development partner
Learn where AI improves coding, debugging, SQL and documentation, and where human review, testing and security discipline must take over.
Course overview
AI can write code. Your job is to make it right
AI assistants are now part of many development environments, but generated code still needs review, testing and security checks. This program teaches a disciplined workflow: describe the task, generate a candidate solution, review it, test it and document the decisions.
You will work on programming, SQL and integration exercises, including a conceptual introduction to embeddings, retrieval-augmented generation (RAG) and tool-using agents. Security, licensing, privacy and code provenance are treated as engineering requirements, not afterthoughts.
What makes this program practical
- Prompt patterns specifically for code and database work.
- Structured review checklists for AI-generated changes.
- Debugging and test-generation exercises.
- A small API prototype that calls an AI service safely.
Tools disclaimer: Sessions explore tools such as ChatGPT, Gemini, GitHub Copilot, Claude and other relevant platforms only where available and relevant to the batch. No program promises coverage of every AI tool or technology.
Curriculum
AI for developers training modules
Ten modules covering coding, SQL, integration and secure practice.
Prompt patterns for code
- Specifying language, version, inputs and constraints.
- Providing interfaces, schemas and example usage.
- Asking for explanations and trade-offs, not just code.
- Iterating with errors and test results.
Code generation, review and refactoring
- Generating functions and small components.
- Reviewing correctness, readability and edge cases.
- Refactoring with behaviour-preserving checks.
- Recognising unsafe or non-idiomatic suggestions.
Debugging with AI
- Describing symptoms, logs and reproduction steps.
- Generating hypotheses and test cases.
- Reading stack traces with AI assistance.
- Confirming the fix instead of accepting it.
AI-assisted SQL and database work
- Turning requirements into SQL with schema context.
- Reviewing joins, filters and aggregation logic.
- Generating test data and validation queries.
- Performance and data-safety checks before execution.
Testing and documentation
- Generating unit-test cases and edge scenarios.
- Writing technical documentation from code.
- Reviewing AI-generated tests for real coverage.
- Keeping documentation aligned with changes.
APIs, embeddings and RAG concepts
- Calling an AI service from application code.
- Tokens, latency, cost and error handling basics.
- Embeddings and semantic search concepts.
- Retrieval-augmented generation at a high level.
Agents, tools and orchestration concepts
- Tool calling and structured outputs.
- Multi-step task design and guardrails.
- When an agent is unnecessary.
- Logging, limits and human approval points.
Security, privacy and licensing
- Secrets, credentials and code exposure risks.
- Data-handling rules for AI services.
- Licensing and provenance of generated code.
- Dependency and vulnerability review.
Capstone: AI-assisted feature and review pack
- Generate and refine a small feature or API integration.
- Write tests and a review checklist.
- Document prompts, assumptions and limitations.
- Present the result and explain human interventions.
Learning outcomes
What you should be able to do
Prompt for code
Give AI assistants enough context to produce relevant, testable code.
Review AI output
Identify logic errors, edge cases and insecure patterns in generated code.
Debug systematically
Use AI to form and test hypotheses rather than guessing randomly.
Validate SQL
Check query logic and performance before running it on real data.
Integrate APIs
Build a controlled prototype that calls an AI service with error handling.
Apply safeguards
Protect secrets, data and licences while using AI in development.
Practical work
Ship a reviewed prototype, not a blind generation
- Generate and refine a function using a structured prompt.
- Review an AI-generated change and document the issues found.
- Write and validate SQL for a sample schema.
- Create tests for edge cases and confirm coverage.
- Build a small API-based AI prototype with error handling.
- Write a team checklist for AI-assisted code review.
Certification note: This is a practical training program with preparation support for relevant topics where applicable. We are not an official certification body.
Enrolment questions
AI for developers FAQ
Which programming language is used?
Examples are language-agnostic where possible, with practical exercises in common languages. The principles apply across languages, and corporate batches can use your team's stack.
Do I need prior AI or machine learning experience?
No. You need programming experience and basic SQL. AI concepts are introduced from the developer's perspective, without requiring a machine learning background.
Will I learn to build a full RAG application?
You will learn RAG concepts and build a small controlled prototype where the batch environment allows. A production-grade RAG system requires additional engineering, evaluation and security work beyond this course.
Which AI tools are covered?
Tools such as ChatGPT, Gemini, GitHub Copilot, Claude and other relevant platforms are explored where available and relevant to the batch. We do not promise coverage of every tool or technology.
Does this replace software engineering fundamentals?
No. AI assistance still depends on your understanding of design, testing, security and maintainability. The course reinforces those fundamentals rather than replacing them.
Related training
Build complementary skills
AI for developers
Add AI to your development workflow
Tell us your stack and experience level. We will confirm the suitable batch and exercises.