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This section provides operational guidance.
| Section | Description |
|---|---|
| How AI code assistants integrate into the SDLC | Overview of deployment patterns, capability maturity, and the distinction between code assistants and agentic pipelines |
| Section | Description |
|---|---|
| AI code assistant instructions | Guidance on creating and managing instruction files for AI code assistants across government projects |
| AI SDLC playbook | Guidance on integrating AI coding assistants across all phases of the software development lifecycle |
| Context engineering | Techniques for providing effective context to AI code assistants to maximise output quality and relevance |
| Legacy system modernisation | Signpost to all guidance and prompts for AI-assisted analysis, planning, and safe modernisation of legacy codebases |
| Model selection | Guidance on selecting the right AI code assistant and model for your task |
| Prompt engineering | A series of guides on prompt engineering best practice |
| Token cost management | Techniques and tools for reducing LLM token consumption and managing costs in agentic workflows |
| Working with constrained context windows | Practical guidance for engineers working with models that have limited context capacity, including older models and those available under procurement or data residency constraints |