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Build a local Python runtime-control layer that uses feature flags to select model routes, prompt versions, tool authority, retrieval corpora, reasoning modes, and kill switches before an AI workflow executes.

Build a .NET data-contract pipeline that admits only fresh, owned, audience-safe, lineage-backed insurance policy documents into RAG or agent context.

Turn chain-of-thought from a prompt habit into an AI engineering contract with private reasoning modes, visible rationales, evidence, logging policy, and evaluation checks.

A practical guide to building an AI engineering portfolio that demonstrates system judgment, operational tradeoffs, and production readiness without relying on flashy demos.

Build a Python tool-result admission gate that rejects unsafe tool output, redacts sensitive fields, packs only admitted context, and uses Microsoft Foundry for final synthesis.

Build a markdown-first governance package that treats golden eval cases as controlled production assets with labeling rules, adjudication, versioning, leakage controls, and release evidence.

Build a Python moderation gate that scores text with Detoxify, asks a local Ollama model for structured review when policy requires it, and keeps the final content-risk decision in deterministic code.

Design a markdown-first documentation context package with llms.txt, source ownership, freshness rules, exclusions, and eval questions so agents can use docs without treating the whole site as context.

Loop engineering moves AI work from one prompt at a time to bounded control systems with triggers, gates, state, budgets, and escalation paths.

JSON, XML, YAML, TOML, and TOON each carry different signals inside a prompt. Knowing what the model actually does with each format is how you make format a deliberate engineering decision instead of an afterthought.