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✅ Factors for Selecting Appropriate Generative AI Models

Choosing the right generative AI model depends on several technical, business, and regulatory considerations. Below are key factors to evaluate:

🧠 Model Types​

  • Definition: Select the model architecture that aligns with your data type and task.
  • Examples:
    • Text: GPT, Claude, LLaMA
    • Image: Stable Diffusion, DALL·E
    • Multi-modal: Gemini, GPT-4 (text + image)

📈 Performance Requirements​

  • Definition: Assess how fast, accurate, and scalable the model needs to be.
  • Considerations:
    • Response time (latency)
    • Token generation speed
    • Throughput for concurrent users
  • Trade-off: Larger models often perform better but are slower and costlier.

🧩 Capabilities​

  • Definition: Determine if the model supports the features your application needs.
  • Examples:
    • Can it follow instructions (instruction tuning)?
    • Does it support multi-turn conversation memory?
    • Can it generate code, translate, or summarize?

🚧 Constraints​

  • Definition: Understand the limitations that could impact implementation.
  • Types:
    • Hardware: GPU/CPU availability
    • Budget: Cost per 1,000 tokens or API usage fees
    • Size: Model size affects deployment (edge vs. cloud)

📜 Compliance & Security​

  • Definition: Ensure the model complies with organizational and legal standards.
  • Examples:
    • Data privacy (GDPR, HIPAA)
    • Content filtering or moderation
    • Explainability requirements in regulated industries

🔁 Customizability​

  • Definition: Evaluate whether the model can be fine-tuned or customized.
  • Options:
    • Out-of-the-box (zero-shot/few-shot)
    • Fine-tuned with domain-specific data
    • Embedding + RAG (retrieval-augmented generation)

🔒 Hosting & Deployment Model​

  • Definition: Choose how and where the model will run.
  • Options:
    • Fully managed API (e.g., AWS Bedrock, OpenAI)
    • Self-hosted on cloud or edge
    • On-premise for sensitive data

💬 Language and Region Support​

  • Definition: Ensure the model supports the target languages and complies with local data handling laws.
  • Examples:
    • Khmer, Japanese, or multilingual capabilities
    • Region-specific data residency requirements