Technical Reference | AI Engineering Playbooks

A practical framework for building an AI evaluation harness that links test quality to release decisions and operational confidence. Evaluation harnesses turn subjective model quality into measurable release criteria. Combine functional, safety, latency, and cost checks into one pipeline. Block releases when critical thresholds are missed, even under delivery pressure. If your AI release decision is based on a demo, you are not releasing engineering software; you are releasing a hope strategy. A proper evaluation harness creates repeatable evidence for quality, safety, and cost trade-offs. Prerequisites Versioned prompts and model configuration. Representative test dataset by use case. CI/CD pipeline with artefact retention. Clear service-level objectives for latency and reliability. Evaluation layers 1) Functional correctness Golden set response checks. Tool invocation correctness. Schema compliance for structured outputs. 2) Safety and policy Prompt in...

A practical workflow for using AI-first coding speed while preserving quality, security, and maintainability. Vibe coding is useful for speed, but speed without controls creates technical debt quickly. This workflow keeps velocity while protecting reliability. The 5-step workflow Intent definition : write a one-paragraph spec before prompting. AI generation : generate initial implementation in small modules. Human review : validate architecture, naming, and boundary decisions. Automated checks : lint, tests, type checks, and security scan. Operational check : logging, error paths, and rollback readiness. Non-negotiable guardrails Never merge AI-generated code without human review. Always require tests for changed behaviour. Always check secrets and auth flows manually. Always capture design rationale for non-obvious choices. Where vibe coding works best Prototypes and internal tools. Boilerplate and repetitive integration code. Test scaffolding and docs g...