AI Integration in Legacy Codebases training course

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