Multi-Target (Parallel) Baking

Multi-target baking lets you bake entire knowledge bases worth of prompts into a single model. Each piece of knowledge (a document, fact, procedure) becomes a target, and you control how much each piece influences the final model through weighted datasets.

Completed your first bake? Multi-target baking builds on that foundation.

Bake Any Knowledge Into Model Weights

Any information you can express as a prompt can be baked into a language model:

Behaviors: “Act like Yoda”, “Be empathetic”, “Write concisely”
Facts: Product documentation, company policies, legal knowledge
Procedures: Troubleshooting guides, how-to instructions, workflows
Expertise: Domain knowledge, technical specifications, best practices

Multi-target baking lets you bake thousands of prompts at once.

How does RAG Compare to Baking?

Traditional RAG

Architecture:

1/ User asks question
2/ Query vector database
3/ Retrieve relevant docs
4/ Inject into prompt context
5/ Generate response

Overhead:

+200ms retrieval latency
+500 tokens per request
Vector DB & inference
Embedding generation

Multi-Target Baking

Architecture:

1/ User asks question
2/ Generate response

Overhead:

0ms (no retrieval)
0 context tokens
Model only
No embeddings needed

Benefits Over RAG Systems

Zero retrieval latency: No database queries at inference time
Zero context tokens: Knowledge lives in weights, not prompts
Simpler architecture: No vector databases, embeddings, or retrieval logic
Offline operation: Model works without external dependencies
Weighted knowledge: Prioritize important information during training
Consistent responses: Deterministic knowledge, not dependent on retrieval quality

Example: Baking Apple Product Knowledge

Traditional RAG systems store product information in a vector database, retrieve relevant docs at runtime, and inject them into prompts. Multi-target baking bakes the knowledge directly into weights.

Your Knowledge Base

Imagine you’re building an Apple support chatbot. Your knowledge base contains: Document 1: iPhone Support

iPhone Troubleshooting:

- iPhone won't turn on: Hold Side + Volume Down for 10 seconds
- Battery drains quickly: In Settings, disable background refresh
- Can't connect to WiFi: Reset network settings in General → Reset

Document 2: Mac Support

Mac Troubleshooting:

- Mac won't start: Press and hold power button for 10 seconds
- Running slow: Check Activity Monitor for memory usage & clear cache
- Bluetooth issues: Turn Bluetooth off/on, remove and re-pair device

Document 3: AirPods Support

AirPods Troubleshooting:

- Won't connect: Put in case, hold button on back for 15 seconds
- Audio cutting out: Clean AirPods speakers, check Bluetooth connection
- Battery drains fast: Disable automatic ear detection in Bluetooth settings

Instead of RAG retrieval, bake each support document as a separate target.

Complete Multi-Target Workflow in 6 Steps

Create Repository & Shared Student Prompt

Set up your repository and the shared empty student prompt:

from aibread import Bread

client = Bread()

# Create repository
client.repo.set(repo_name="apple_support_agent")

# Shared student prompt (empty for always-on)
client.prompts.set(
    prompt_name="empty_student_prompt",
    repo_name="apple_support_agent",
    messages=[{"role": "system", "content": ""}]
)

In this example, we use a shared empty student prompt since we wish that this model always responds as an Apple customer support agent.

Create iPhone Support Target

Bake iPhone troubleshooting knowledge:

# iPhone Support Knowledge
client.prompts.set(
    prompt_name="iphone_teacher_prompt",
    repo_name="apple_support_agent",
    messages=[{
        "role": "system",
        "content": """
        You are an Apple iPhone support expert. You know:
        
        iPhone Troubleshooting:
        - iPhone won't turn on: Hold Side + Volume Down for 10 seconds
        - Battery drains quickly: In Settings, disable background refresh
        - Can't connect to WiFi: Reset network settings in General → Reset
        """
    }]
)

client.targets.set(
    target_name="iphone_support",
    repo_name="apple_support_agent",
    template="default",
    overrides={
        "generators": [
            {
                "type": "hardcoded",
                "numq": 3,
                "questions": [
                    "My iPhone won't turn on",
                    "Why is my iPhone battery draining so fast?",
                    "My iPhone screen is frozen"
                ]
            },
            {
                "type": "oneshot_qs",  # LLM-generated stim prompts
                "numq": 100,
                "model": "claude-sonnet-4-5-20250929",
                "temperature": 0.6
            }
        ],
        "teacher_prompt": "iphone_teacher_prompt",
        "student_prompt": "empty_student_prompt"
    }
)

# Generate stim data
client.targets.stim.run(
    target_name="iphone_support",
    repo_name="apple_support_agent"
)
# Once stim complete, generate rollout data
client.targets.rollout.run(
    target_name="iphone_support",
    repo_name="apple_support_agent"
)

Create Mac Support Target

Bake Mac troubleshooting knowledge:

# Mac Support Knowledge
client.prompts.set(
    prompt_name="mac_teacher_prompt",
    repo_name="apple_support_agent",
    messages=[{
        "role": "system",
        "content": """
        You are an Apple Mac support expert. You know:
        
        Mac Troubleshooting:
        - Mac won't start: Press and hold power button for 10 seconds
        - Running slow: Check Activity Monitor for memory usage & clear cache
        - Bluetooth issues: Turn Bluetooth off/on, remove and re-pair device
        """
    }]
)

client.targets.set(
    target_name="mac_support",
    repo_name="apple_support_agent",
    template="default",
    overrides={
        "generators": [
            {
                "type": "hardcoded",
                "numq": 3,
                "questions": [
                    "My Mac won't start up",
                    "Why is my Mac running so slow?",
                    "My Mac app keeps crashing"
                ]
            },
            {
                "type": "oneshot_qs",  # LLM-generated stim prompts
                "numq": 100,
                "model": "claude-sonnet-4-5-20250929",
                "temperature": 0.6
            }
        ],
        "teacher_prompt": "mac_teacher_prompt",
        "student_prompt": "empty_student_prompt"
    }
)

client.targets.stim.run(
    target_name="mac_support",
    repo_name="apple_support_agent"
)
client.targets.rollout.run(
    target_name="mac_support",
    repo_name="apple_support_agent"
)

Create AirPods Support Target

Bake AirPods troubleshooting knowledge:

# AirPods Support Knowledge
client.prompts.set(
    prompt_name="airpods_teacher_prompt",
    repo_name="apple_support_agent",
    messages=[{
        "role": "system",
        "content": """
        You are an Apple AirPods support expert. You know:
        
        AirPods Troubleshooting:
        - Won't connect: Put in case, hold button on back for 15 seconds
        - Audio cutting out: Clean AirPods speakers, check Bluetooth connection
        - Battery drains fast: Disable automatic ear detection in Bluetooth settings
        """
    }]
)

client.targets.set(
    target_name="airpods_support",
    repo_name="apple_support_agent",
    template="default",
    overrides={
        "generators": [
            {
                "type": "hardcoded",
                "numq": 3,
                "questions": [
                    "My AirPods won't connect to my iPhone",
                    "Why does the audio keep cutting out?",
                    "One of my AirPods isn't working"
                ]
            },
            {
                "type": "oneshot_qs",  # LLM-generated stim prompts
                "numq": 100,
                "model": "claude-sonnet-4-5-20250929",
                "temperature": 0.6
            }
        ],
        "teacher_prompt": "airpods_teacher_prompt",
        "student_prompt": "empty_student_prompt"
    }
)

client.targets.stim.run(
    target_name="airpods_support",
    repo_name="apple_support_agent"
)
client.targets.rollout.run(
    target_name="airpods_support",
    repo_name="apple_support_agent"
)

Each document from your knowledge base becomes a target. Scale to dozens or hundreds of targets - each Apple product, feature, or troubleshooting guide would get its own target.

Combine Targets & Configure Bake

Combine targets & specify bake config (in this example, set equal weightage to all baked prompts):

bake = client.bakes.set(
    bake_name="apple_support_agent_v1",
    repo_name="apple_support_agent",
    template="default",
    overrides={
        "datasets": [
            {
                "target": "iphone_support",
                "weight": 0.33
            },
            {
                "target": "mac_support",
                "weight": 0.33
            },
            {
                "target": "airpods_support",
                "weight": 0.33
            }
        ]
    }
)

Run Training

Start the multi-target bake:

client.bakes.run(
    bake_name="apple_support_agent_v1",
    repo_name="apple_support_agent"
)

RAG vs Baked Knowledge

Traditional RAG	Multi-Target Baking
User asks: “My iPhone won’t turn on” Query vector database Retrieve iPhone doc (200ms) Inject into prompt (500 tokens) Generate response Latency: +200ms Tokens: +500 context tokens Infrastructure: Vector DB + embeddings	User asks: “My iPhone won’t turn on” Generate response (knowledge in weights) Latency: 0ms overhead Tokens: 0 context tokens Infrastructure: Model only

The model just “knows” iPhone troubleshooting. It’s baked into the weights, not retrieved.

Best Practices

Generate Diverse Training Data

Use good generators for each target to create diverse, high-quality training examples.

Test Incrementally

Start with 2 targets, validate results, then add more. Easier to debug with fewer targets.

Common Use Cases for Knowledge Baking

Replace Product Documentation RAG

Bake your entire product docs into the model:

Target 1: “Product Overview”
Target 2: “Feature Documentation”
Target 3: “API Reference”
Target 4: “Troubleshooting”
Target 5: “FAQs”

Result: Customer support chatbot with instant, zero-latency documentation access.

Bake Company Policies

Eliminate policy manual lookups:

Target 1: “HR Policies”
Target 2: “Code of Conduct”
Target 3: “Benefits Information”
Target 4: “Onboarding Procedures”

Result: HR assistant that knows all corporate policies.

Bake Educational Content

Create subject-matter expert models:

Target 1: “Biology Chapter 1: Cells”
Target 2: “Biology Chapter 2: Genetics”
Target 3: “Biology Chapter 3: Evolution”
Target 4: “Biology Chapter 4: Ecosystems”
Target 5: “Biology Chapter 5: Anatomy”

Result: Tutoring model with textbook knowledge baked in.

Bake Legal Knowledge Base

Build specialized legal assistants:

Target 1: “Contract Law Basics”
Target 2: “Intellectual Property Guide”
Target 3: “Employment Law”

Result: Legal research assistant without needing legal database queries.

Next Steps

Iterative Baking

Refine multi-target models with additional bakes

Production Patterns

Add polling and error handling for production

Bake Configuration

Customize hyperparameters for multi-target training

Generators

Generate diverse training data for each target

Getting Started

Baking Guides

Using Baked Models

SDK Guides

Configuration

Bake Any Knowledge Into Model Weights

How does RAG Compare to Baking?

Traditional RAG

Multi-Target Baking

Benefits Over RAG Systems

Example: Baking Apple Product Knowledge

Your Knowledge Base

Complete Multi-Target Workflow in 6 Steps

RAG vs Baked Knowledge

Best Practices

Common Use Cases for Knowledge Baking

Replace Product Documentation RAG

Bake Company Policies

Bake Educational Content

Bake Legal Knowledge Base

Next Steps

Iterative Baking

Production Patterns

Bake Configuration

Generators

Getting Started

Baking Guides

Using Baked Models

SDK Guides

Configuration

​Bake Any Knowledge Into Model Weights

​How does RAG Compare to Baking?

Traditional RAG

Multi-Target Baking

​Benefits Over RAG Systems

​Example: Baking Apple Product Knowledge

​Your Knowledge Base

​Complete Multi-Target Workflow in 6 Steps

​RAG vs Baked Knowledge

​Best Practices

​Common Use Cases for Knowledge Baking

​Replace Product Documentation RAG

​Bake Company Policies

​Bake Educational Content

​Bake Legal Knowledge Base

​Next Steps

Iterative Baking

Production Patterns

Bake Configuration

Generators

Bake Any Knowledge Into Model Weights

How does RAG Compare to Baking?

Benefits Over RAG Systems

Example: Baking Apple Product Knowledge

Your Knowledge Base

Complete Multi-Target Workflow in 6 Steps

RAG vs Baked Knowledge

Best Practices

Common Use Cases for Knowledge Baking

Replace Product Documentation RAG

Bake Company Policies

Bake Educational Content

Bake Legal Knowledge Base

Next Steps