Guardrails
How production AI systems constrain, validate, refuse, and recover from unsafe or low-quality behavior.
Planned
Production AI
Guardrails, security, observability, and cost — what keeps AI systems alive in production.
Planned
Coming soonThese will slot into the reading order above as they're published.
Prompt Injection And Security
How hostile inputs try to override instructions, leak data, or misuse tools.
Observability And Tracing
How to trace prompts, retrieval, tools, latency, cost, and quality through an AI workflow.
Cost And Latency Engineering
How token budgets, routing, caching, batching, and fallbacks shape AI product economics.
Semantic Caching
When AI systems reuse similar answers or retrieval work, and where correctness risks appear.
Rate Limits, Retries, And Fallbacks
How AI systems behave when providers, tools, or model-serving pools slow down or fail.
Evals In CI
How teams catch prompt, retrieval, tool, and model regressions before release.
Privacy And Data Boundaries
How AI systems keep customer data, retrieved context, logs, and tool outputs inside the right boundaries.
Do You Need A Framework At All?
How to decide whether an AI framework helps your system or hides the real engineering work.
LangChain In Plain Terms
What LangChain is useful for, what it abstracts, and what engineers still need to understand.
LlamaIndex In Plain Terms
How LlamaIndex fits document, data, retrieval, and RAG workflows.
LangGraph In Plain Terms
How graph-shaped agent workflows model stateful, long-running AI tasks.
CrewAI In Plain Terms
How role-based agent teams are framed, and what coordination tradeoffs they introduce.