Research Methods
9 articles
A Pre-Analysis Plan for Your Coding Agent
A three-layer architecture for keeping reasoning agents disciplined: rule, gate, and verification.
What Agents Actually Do (And What They Don't)
An agent is a specification-bounded process. Its output quality depends on prompt precision and project context, not hidden model capabilities.
What We Mistake for AI Capability
AI output quality tracks specification precision, not model capability. A task with wide tolerance makes the model look smart. Narrow tolerance reveals the gaps.
Research Phases Need Different Prompts
Exploration, implementation, and documentation require different AI prompting strategies. Match the prompt to the phase.
The Verification Tax: Every AI Output Needs Checking
Every AI output needs checking. Building verification into workflow to catch hallucinations before they compound.
Context Window Budgeting: Treating Tokens as a Finite Resource
Treating tokens as a finite resource, and knowing when to spawn agents versus work directly.
End-of-Session Hygiene: What to Capture Before Context Resets
What to capture before context resets, and how five minutes of capture saves twenty minutes tomorrow.
Fix Claude Visualizations with Antigravity Prompts
When Claude and ChatGPT botch research figures, structured Antigravity prompts produce the publication-quality graphics we actually need.
The Cold Start Problem: Why the First Five Minutes Matter Most
Why the first five minutes of an AI session matter most, and how CLAUDE.md solves the context problem.