1. Questions for Integration into Applications:
- What additional resources does your model need?
- Does your model need to interact with external data or other applications?
- How will you connect to those resources?
2. Retrieval-Augmented Generation (RAG):
- RAG is used to augment LLMs with external data sources.
- Helps with outdated knowledge: Keeps the model up to date without needing retraining.
- Provides context to improve factuality and avoid hallucinations.
3. Handling Outdated Knowledge:
- RAG accesses additional external data at inference time, reducing the need for re-training.
- This improves the relevance and accuracy of model completions.
4. Business Objectives and Application Design:
- How will the model be consumed?
- What will be the design of the application or API interface?
- Business goals must be defined with clear metrics for success.
- Infrastructure: Ensure proper infrastructure to support the model and application.
5. Infrastructure Layer:
- Provides compute, storage, and network for hosting LLMs and the application.
- Ensure security for data handling throughout the AI lifecycle.
6. Choosing the Right LLM and Infrastructure:
- Select the right LLM for your application and appropriate infrastructure.
- Consider real-time or near-real-time interaction needs with the model.
- You might need additional storage for user completions or feedback for fine-tuning.
7. Additional Tools and Frameworks:
- Model hubs: For managing and sharing models for applications.
- User Interface: Design the interface (website or API) to ensure secure connections.
- Security is critical for data isolation and model access.
8. User Interactions with the Application:
- Users interact with the stack through APIs or a user interface.
- Ensure security for both human and system users.
