Best-Practice

TL;DR Prompt engineering, token efficiency, and structured outputs are the core skills for working effectively with any AI model System design patterns - streaming, caching, structured outputs, graceful fallbacks - matter as much as prompting fluency Testing and validation in AI systems requires clear evaluation criteria and production monitoring, not just pre-launch checks Official documentation from model providers (Anthropic, OpenAI, Google) is the most reliable source of best practices The curated resources table covers everything from GitHub Copilot to local model deployment with Ollama The AI landscape is evolving rapidly, and knowing where to find reliable guidance on best practices has become essential for developers, researchers, and organizations. This post curates the most valuable resources and practices that will help you work more effectively with modern AI systems. ...

The views in this post are my own personal reflections on the industry, written in my own time. They are not about any specific employer, team, or colleague, past or present, and do not draw on any non-public information. “Best practice” is a phrase that should be treated with suspicion. What works for a fintech running 500 engineers rarely works for a five-person startup. The notes below are generic patterns drawn from public talks, books, and industry write-ups - always weighed against context, team size, and what the system is actually trying to do. ...