300: Model Adaptation Techniques

Topic Overview

Once you have selected a Foundation Model, the next step is to adapt it to perform your specific task effectively. Model Adaptation is the process of modifying or guiding a model's behavior to achieve a desired outcome.

This Map of Content (MOC) organizes the three primary adaptation techniques, from the simplest to the most complex, based on the AI Engineer's Decision Framework.

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The Adaptation Spectrum

The three main adaptation techniques exist on a spectrum of complexity, cost, and the type of problem they solve. Choosing the right one is a critical strategic decision.

graph LR
    A[Prompt Engineering] --> B[Retrieval-Augmented Generation RAG]
    B --> C[Fine-Tuning]

    subgraph "Lightweight & Fast"
        A
    end

    subgraph "Moderate Complexity"
        B
    end

    subgraph "Intensive & Powerful"
        C
    end

    style A fill:#e8f5e9,stroke:#1B5E20,stroke-width:2px
    style B fill:#fff3e0,stroke:#f57c00,stroke-width:2px
    style C fill:#fce4ec,stroke:#c2185b,stroke-width:2px

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Choosing the Right Adaptation Technique

🎯 Prompt Engineering

Best for: Task-specific instructions, output formatting, style control - Complexity: Low - Cost: Minimal - Time to implement: Minutes to hours - Use cases: Content generation, classification, basic reasoning

🔍 Retrieval-Augmented Generation (RAG)

Best for: Knowledge-intensive tasks, domain-specific information - Complexity: Moderate - Cost: Moderate (infrastructure + embeddings) - Time to implement: Days to weeks - Use cases: Question answering, document analysis, knowledge bases

🎨 Fine-Tuning

Best for: Specialized behavior, domain adaptation, performance optimization - Complexity: High - Cost: High (compute + data preparation) - Time to implement: Weeks to months - Use cases: Specialized domains, custom reasoning patterns, model compression

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Decision Framework

flowchart TD
    Start([Need to adapt a model?]) --> Q1{Do you need external knowledge?}

    Q1 -->|No| Q2{Is the task complex or specialized?}
    Q1 -->|Yes| RAG[Consider RAG]

    Q2 -->|No| PE[Start with Prompt Engineering]
    Q2 -->|Yes| Q3{Do you have training data and compute resources?}

    Q3 -->|Yes| FT[Consider Fine-Tuning]
    Q3 -->|No| PE

    PE --> Success{Good enough performance?}
    RAG --> Success
    FT --> Success

    Success -->|Yes| Done[✅ Deploy]
    Success -->|No| Iterate[🔄 Iterate or combine techniques]

    style Start fill:#e3f2fd,stroke:#1976d2
    style Done fill:#e8f5e9,stroke:#1B5E20
    style PE fill:#e8f5e9,stroke:#1B5E20
    style RAG fill:#fff3e0,stroke:#f57c00
    style FT fill:#fce4ec,stroke:#c2185b

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Interactive Learning Path

📚 Core Concepts

1. 301: Prompt Engineering - Start here for immediate results
2. 302: Advanced Prompting Techniques - Unlock complex reasoning
3. 303: Prompt Security and Attacks - Protect your applications

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Pro Tip: Start Simple

Always begin with prompt engineering. It's the fastest way to validate your approach and often provides surprisingly good results. You can always add complexity later if needed.

Common Pitfall

Don't jump to fine-tuning without first exhausting prompt engineering possibilities. Many problems that seem to require fine-tuning can be solved with clever prompting and good examples.