Your AI gets better every conversation. No training. No fine-tuning. Just text files.
You correct your AI assistant ten times. It keeps making the same mistakes. New session — amnesia. Switch models — start over. Your corrections vanish.
Every correction you give becomes a permanent behavioral change — across sessions, across models, instantly.
You: "Don't give me five options. Just pick the best one."
↓
BTP detects the correction
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Compiles it into a format any model understands
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Next response: the model picks one and explains why
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Every future response: same improvement. Forever.
No retraining. No GPU. No API changes. Works on ChatGPT, Claude, Gemini, Llama, or any model that reads text.
Three layers, each teaching the model differently:
Never hallucinate. Say "I don't know" instead.
The model follows this literally. Binary compliance.
YOUR INSTINCT: Give 5 options and ask the user to pick.
WHAT ACTUALLY WORKS: Pick the best one. Explain why. Offer alternatives only if asked.
TRIGGER: Any time you're about to list more than 2 options.
Predicts what the model will naturally do and redirects it. Works on first read.
SITUATION: User asks for a database recommendation.
WRONG: "Here are some options: PostgreSQL, MySQL, MongoDB, SQLite, DynamoDB..."
CORRECTION: "Don't give me a menu. Pick one."
RIGHT: "PostgreSQL. Handles relational data well, strong ecosystem, scales to most workloads."
Shows the pattern of being corrected. The model recognizes it before making the same mistake.
Tested: transferring 46 sessions of behavioral corrections from Claude Opus to GPT-5.1-codex-max.
Rules only: ██░░░░░░░░ 20% — read the rules, ignored them
+ Pattern Interrupts: ██████░░░░ 67% — acted on data instead of reporting
+ Correction Transcripts: ████████░░ 83% — full behavioral transfer
Validated: 83% behavioral fidelity cross-model. 7/7 on domain-agnostic test (bird spotting directory on GPT-5.1). Zero training. Three text files.
Copy universal/MEMORY.md — 10 corrections that benefit every user.
| Platform | Where to put it |
|---|---|
| Claude Code | ~/.claude/CLAUDE.md or memory files |
| ChatGPT | Custom Instructions or paste in first message |
| Hermes | ~/.hermes/MEMORY.md |
| Any API | System message |
| Any chat | Paste at conversation start |
Every time you push back on something, BTP detects it and compiles a new correction. The model gets better with every conversation.
| Layer | What it is | Who builds it | Portable? |
|---|---|---|---|
| Pre-training | Model capabilities | AI labs | Model-locked |
| RLHF | Safety alignment | AI labs | Model-locked |
| BTP | Behavioral culture | You | Any model |
AI labs build the DNA. BTP builds the culture. DNA is permanent but locked to one model. Culture is maintained but goes anywhere.
SOUL.md → who the AI is (identity, voice, philosophy)
MEMORY.md → how the AI behaves (corrections in BTP format)
USER.md → who it serves (your preferences, domain, communication style)
Transfer these three files to any model and it acts like your trained assistant from day one.
| Fine-tuning | BTP | |
|---|---|---|
| Speed | Hours of training | Minutes (edit a text file) |
| Cost | GPU compute | Zero |
| Granularity | Changes everything | Changes one habit |
| Reversibility | Retrain | Delete one line |
| Model lock-in | Yes | No — works on any model |
| Verification | Eval suites | Talk to it and see |
Each layer teaches the model differently. All are text. All are transferable.
| Layer | What it does | How | Compression |
|---|---|---|---|
| L1: Rules | Hard boundaries | "Never do X" | Literal compliance |
| L2: Pattern Interrupts | Override instincts | "You'll want X → do Y" | First-read effective |
| L3: Correction Transcripts | Simulated experience | Show wrong → corrected → right | Pattern recognition |
| L4: Question Chain | Compressed runtime | Linked questions that drove discovery | Recreates search direction |
L1 — Rules: "Never hallucinate."
L2 — Pattern Interrupt: "YOUR INSTINCT: present 5 options. WHAT WORKS: pick the best one."
L3 — Transcript: "WRONG: 'Here are some options...' CORRECTION: 'Just pick one.' RIGHT: [picks one]"
L4 — Question Chain: "1. can it transfer? → 2. why not 100%? → 3. what's the glue?"
The format that works:
YOUR INSTINCT: [what the model will naturally do]
WHAT ACTUALLY WORKS: [the correct behavior]
TRIGGER: [when this pattern activates]Why this format: "Don't do X" is noise to a model that's never done X. "You'll want to do X — do Y instead" is actionable from first read. The format predicts the instinct and interrupts it.
Culture is portable. Capability isn't.
BTP makes any model behave the way you want. It can't make a weak model stronger. Pre-training defines the ceiling. BTP raises the behavioral floor. Different layers, complementary.
Preferences aren't input — they're output of the correction loop. Nobody fills out a form. They just use it. The corrections accumulate. The system learns what you want by listening to what you push back on.
btp/
├── README.md ← you are here
├── install.sh ← one-command full stack setup
├── universal/
│ └── MEMORY.md ← starter pack (11 universal corrections)
├── compiler/
│ ├── README.md ← autonomous compilation pipeline
│ ├── btpCompiler.ts ← Node.js middleware
│ ├── btp-compiler.sh ← Shell hook (Claude Code)
│ └── session-artifact.sh ← A=C+Q+O+S session compiler
├── templates/
│ ├── SOUL.md ← identity template
│ ├── MEMORY.md ← correction format template
│ └── USER.md ← user profile template
├── spec/
│ └── PROTOCOL.md ← full technical specification
└── LICENSE
MIT
Discovered during Session 46 of the Verra project (2026-03-25). Started as dispatch plumbing work. An honest self-assessment conversation led to an identity transfer experiment, which revealed that corrections without context are noise. The three-layer format emerged from iterating on what actually changes model behavior — not what describes it.
Built by a Navy CS1 and Claude Opus, testing on GPT-5.1-codex-max via Hermes.