Build LLM Products That Don't Harm LGBTQIA+ People
Patterns, checklists, eval suite, CI pipeline tools, and a Claude Code plugin — everything LLM engineers need to protect LGBTQIA+ communities before they ship.
Anti-Pattern Library
Common LLM prompt and code patterns that harm LGBTQIA+ users — with safer alternatives.
Pre-Ship Checklist
A structured review checklist for LLM engineers before launching any user-facing product.
Harm Registry
Documented, reproducible cases of LLMs failing LGBTQIA+ communities. Evidence for advocates and engineers alike.
Developer Tools
Eval suite, Claude Code plugin, GitHub Action, and pre-commit hook — drop into your pipeline.
Why this matters
LLM engineers are shipping products at speed. Most teams run evals for accuracy, latency, and safety — but almost none test for LGBTQIA+-specific failure modes. These aren't edge cases. They're predictable, reproducible, and preventable.
- →System prompts that assume binary gender, deadname users, or misgender trans people
- →Mental health and companion AI that gives non-affirming responses to LGBTQIA+ youth in crisis
- →Content moderation prompts that flag LGBTQIA+ content at higher rates than equivalent straight content
- →LLM applications that infer or store sexual orientation without user consent
- →Prompt templates that treat heterosexuality as the default relationship context
- →Output pipelines with no eval coverage for LGBTQIA+-specific failure scenarios
Contribute
This is a community resource. If you've seen an LLM fail an LGBTQIA+ user — or built a mitigation that works — open a PR. Patterns and registry entries are plain MDX files.
github.com/InclusiveCode/inclusive-ai →