"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"
Brian Leek, Data Analyst, May 2022
Integration mapping workshop:
reviewing a real codebase together to find where AI adds the most value with the least disruption
Abstraction patterns:
service interfaces, adapters, and facades that isolate AI calls cleanly from existing business logic
Secrets management in older environments: environment variables, vault tools, and a clear list of what not to do
Async patterns lab:
threading, queuing, and keeping the user interface responsive while waiting on AI response times
Language-specific labs:
adding a working AI feature to a Java Spring, .NET, or Python Django application chosen by participants
Pitfall clinic:
real examples of token overflow, prompt injection in production, and over-confident AI output in critical code paths
Documentation standards:
annotating AI-integrated code so the next developer understands what is happening and why
Performance benchmarking:
measuring before and after latency, cost per API call, and a practical method to optimise both
Graceful degradation:
fallback logic, cached responses, and user-facing messages when AI services are slow or unavailable
Integration test writing:
mocking AI responses reliably, testing edge cases, and avoiding flaky tests caused by non-determinism
Integration test writing:
mocking AI responses reliably, testing edge cases, and avoiding flaky tests caused by non-determinism
|
Developers and Engineers |
"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"
Brian Leek, Data Analyst, May 2022
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This practical course teaches how to integrate AI capabilities into existing software systems with minimal disruption and maximum reliability.
Participants will learn how to identify high-impact integration points within real codebases where AI can add value effectively.
The course covers architectural patterns such as adapters, facades, and service interfaces to safely isolate AI functionality from core business logic.
Learners will implement secure and maintainable AI integrations, including secrets management, async processing, and performance optimisation techniques.
Hands-on labs include adding AI features to real applications using frameworks like Java Spring, .NET, and Python Django.
The course also explores common production pitfalls such as prompt injection, token limits, latency issues, and unreliable AI outputs.
By the end of the course, participants will be able to integrate, test, and maintain AI features inside legacy systems with confidence and engineering discipline.
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