Prompt Baking encodes prompt behavior directly into model weights. Fine-tune AI models with the convenience of prompt engineering. You shouldn’t need a PhD to teach your AI what it needs to know or wait for someone else’s model update to give you what you need. With Bread, your model permanently inherits the prompted behavior with zero input tokens at inference time.Documentation Index
Fetch the complete documentation index at: https://docs.bread.com.ai/llms.txt
Use this file to discover all available pages before exploring further.
The value: After baking, your model can exhibit prompted behavior with zero input tokens. The skills live in the weights, not in runtime prompts.
How does Baking work?
Learn the four-phase process to baking
What is bgit?
Learn the git-native interface to baking
Quickstart
Run a full bake in 5 minutes
Advanced Features
Configure generators, targets, and advanced baking options
Core Concepts
Repositories
Like GitHub, these repos hold all of a model’s bake data
Prompts
The actual prompts you want to “bake in”
Stim Data
User prompts the prompted model would receive
Rollout Jobs
Responses from the prompted model to the stim data
Targets
The composition of stim & rollout for a given prompt
Bakes
Configuration for your bake with rollout data
Full Process: Prompt, Stim, Rollout, Bake
Requirements
- Python: 3.8 or higher
- Dependencies: httpx (included), optional aiohttp for async performance
- API Key: Required for authentication
Need Help?
GitHub Repository
Check out our public repo to start baking today
Workflows Guide
Learn common patterns and workflows
Error Handling
Handle errors gracefully
Client Configuration
Unlock powerful SDK capabilities