tech-decision

Evidence-based technical decisions require defining weighted criteria, gathering parallel evidence from official docs and community sources, and synthesizing tradeoffs to produce a conclusion-first recommendation report. This approach clarifies ambiguous library selection choices.

Comparing architecture patterns involves defining specific engineering criteria with weights, evaluating source-attributed evidence for each option, and synthesizing tradeoffs to generate a final recommendation with explicit risks and considerations for your project context.

A technical decision report should be conclusion-first, surfacing the winning option immediately, then detailing criteria and weights, option pros and cons with source attribution, and explicit risks and considerations to explain why it wins.

Yes, you can evaluate implementation approaches like REST vs GraphQL by defining weighted evaluation criteria, gathering evidence from codebase patterns and community perspectives, and synthesizing the tradeoffs to recommend the best option for your stated engineering choice.

You need formal tradeoff analysis when facing ambiguous engineering choices like polling vs WebSocket, where defining criteria with weights and gathering multi-angle evidence ensures a confident, source-attributed recommendation rather than subjective selection.

Automated evidence synthesis relies on defined criteria and available sources, meaning it provides a structured recommendation with risks and considerations based on current information, but requires you to validate project-specific constraints and codebase patterns.