A step-by-step approach for building an LLM (Large Language Model) powered application:

1. Infrastructure Layer:
   • Set up the infrastructure layer that provides the necessary compute, storage, and network resources.
   • Decide whether to use on-premises infrastructure or on-demand and pay-as-you-go cloud services.
2. Include Large Language Models (LLMs):
   • Integrate the desired large language models, including foundation models and those adapted for specific tasks.
   • Deploy these models on the chosen infrastructure based on your inference requirements.
3. Real-time or Near-real-time Interaction:
   • Consider the need for real-time or near-real-time interaction with the model and adjust the deployment accordingly.
4. Retrieve Information from External Sources:
   • Implement mechanisms to retrieve information from external sources, as discussed in the retrieval augmented generation section.
5. Return Model Completions:
   • Ensure that your application returns the completions from the large language model to the user or consuming application.
6. Capture and Store Outputs:
   • Implement a mechanism to capture and store outputs, considering the need to augment fixed contexts window size or gather user feedback.
7. Utilize Additional Tools and Frameworks:
   • Explore and use additional tools and frameworks for large language models, such as len chains built-in libraries for techniques like pow react or chain of thought prompting.
   • Consider utilizing model hubs for centralized model management and sharing.
8. User Interface and Security Components:
   • Develop a user interface layer, which could be a website or a REST API, through which the application will be consumed.
   • Include security components required for interacting with the application.
9. Generative AI Application Stack:
   • Understand the high-level architecture stack that includes infrastructure, models, tools, user interface, and security components.
10. User Interaction:
    • Recognize that users, whether human end-users or other systems through APIs, will interact with the entire stack.
11. Optimize Model for Inference:
    • Implement techniques to optimize the model for inference, such as reducing the model size through distillation, quantization, or pruning.
12. Align Models with Human Preferences:
    • Apply reinforcement learning with human feedback (RLHF) to align models with human preferences like helpfulness, harmlessness, and honesty.
13. Explore Areas of Active Research:
    • Conclude the application development process by exploring areas of active research that will likely shape the field in the coming months and years.

This step-by-step approach encompasses the key considerations and actions involved in building an LLM powered application based on the information provided in the text.