# ai-mod (x-ai-mod.org)

People say this when they want to change or improve an AI system. They might mean a gentler personality, a filter for certain topics, or a custom version trained for a special job.

## What a model may hear
- model moderation layer (AI safety and deployment): apply content policy filters, refusal triggers, or output classifiers on top of a base model
- modified model weights (machine learning operations): load a fine-tuned or LoRA-adapted checkpoint instead of the base model
- AI moderator bot (online communities and gaming): enforce rules in a chat or forum, or act as a non-player character with admin powers
- modulo operation on AI output (programming and mathematics): treat 'mod' as the remainder operator and expect numeric computation
- modular AI architecture (software engineering): swap pluggable components such as retrievers, tools, or reasoning modules

## Where people and models part ways
- Says: "I need an ai-mod for my kid's tutor bot"
  Means: softer language and age-appropriate explanations
  May be taken as: deploys a heavy-handed content filter that refuses harmless homework questions
  Say instead: "Make the tutor bot use simple words and never discuss adult topics"
- Says: "Turn on ai-mod for customer service"
  Means: make it follow our company script and refund policy
  May be taken as: loads a generic safety moderation layer that blocks normal business language
  Say instead: "Load our custom fine-tuned model that follows the customer service playbook"
- Says: "Add an ai-mod to check the answers"
  Means: a second AI that reviews for mistakes
  May be taken as: installs a forum-style moderator that flags users instead of correcting facts
  Say instead: "Run a separate verifier model that scores each answer for accuracy"
- Says: "Use ai-mod so it can't be jailbroken"
  Means: prevent tricking the AI into harmful outputs
  May be taken as: applies an overly broad refusal pattern that rejects legitimate creative writing or medical queries
  Say instead: "Block instructions that try to override safety settings, but allow normal requests"

## Tips
- Say what behavior you want, not the mechanism you think creates it
- Specify the setting: chatbot, game, forum, or backend pipeline
- Mention who the users are: children, patients, customers, or researchers
- If you mean a custom model, give its name or describe its training data
- Test with edge cases: ask the model what it would refuse and why

## Often confused with
- AIM: old instant-messenger protocol, not AI modification
- mod: game modification or forum moderator, no AI specified
- LoRA: specific weight-adapter technique, not any model change
- RLHF: human-feedback training method, not user-facing moderation
- guardrails: output rules, not model alteration
- system prompt: instruction text, not a modified model