Techniques, patterns, mental models, and anti-patterns for writing effective prompts.
12 articles
Five specific anti-patterns with examples: vague instructions, over-constraining, context dumping, ignoring output format, and treating all ...
Context window management is an economics problem. You have a fixed budget, every token costs something, and ROI varies dramatically.
A line-by-line breakdown of a real system prompt: role definition, constraints, output format, examples, and context boundaries.
Prompt engineering is a craft discipline with patterns, anti-patterns, testing, iteration, and production concerns. Here's what that actuall...
Prompt engineering has layers, just like a software stack. Understanding which layer to optimize changes everything.
Chain-of-thought prompting improves complex reasoning but wastes tokens and adds latency on simple tasks. Here's how to know the difference.
Prompts change. Without versioning, you can't test, compare, or roll back. Here's how to bring software engineering discipline to prompt man...
A prompt library isn't a folder of text files. It's a structured database with 68 categories, typed columns, and composable variables.
Some prompt techniques transfer perfectly across models. Others fail spectacularly. Here's a practical guide to what works where.
The difference between useful AI output and noise is structure. Here are the patterns that make LLMs return exactly what your system needs.
Role, context, constraints, format, examples, edge cases, fallbacks, and more. A complete checklist for system prompts that actually work.
Isolation testing, token analysis, output comparison, and systematic elimination. A debugging methodology for prompts that aren't working.