Copilot
Certainly! Let’s explore the emerging architecture of today’s Large Language Models (LLMs), including microservices and microfrontends, as well as problem areas you can start exploring:
Building LLM Applications:
When building software with LLMs (or any machine learning model), the process differs significantly from traditional software development.
Rather than compiling source code into binary, developers work with datasets, embeddings, and parameter weights to generate consistent and accurate outputs.
LLM outputs are probabilistic and don’t produce the same predictable outcomes as traditional code execution.
Five Steps to Building an LLM App:
Focus on a Single Problem: Start with a well-defined problem that’s focused enough for quick iteration but substantial enough to impress users. For example, GitHub Copilot initially focused on coding functions in the IDE.
Choose the Right LLM: Consider licensing, model size, and performance. Smaller LLMs are challenging conventional wisdom by providing improved quality predictions while being faster and cheaper.
Customize Your LLM: Techniques like fine-tuning and in-context learning allow you to adapt pre-trained models to specific tasks.
Evaluate Model Performance: Use offline evaluations to measure how well and consistently your LLM generates desired output.
Iterate and Improve: Continuously refine your LLM app based on user feedback and real-world usage.
Microservices and Microfrontends:
Microservices Architecture: Break down your LLM app into smaller, independent services (microservices). Each microservice handles a specific function or feature.
Microfrontends: Similarly, divide the frontend into smaller, self-contained parts (microfrontends). This allows independent development and deployment of UI components.
Lanarky: The Web Framework for LLM Microservices:
Lanarky is a web framework specifically designed for LLM developers.
It guarantees zero vendor lock-in with any LLM tooling frameworks or cloud providers.
Built on top of FastAPI, Lanarky offers modern features for LLM-powered microservices1.
End-of-Life Management in LLM-Powered AI:
Explore the integral role of microservices in managing end-of-life challenges for LLM systems.
Scalable and secure solutions are crucial for maintaining LLM-powered applications in an ever-evolving landscape2.
In summary, the architecture of LLM applications involves thoughtful problem selection, model customization, and a shift toward microservices and microfrontends. Dive into these areas to unlock the potential of LLMs! 🚀🔍 The architecture of today’s LLM applications - The GitHub Blog ajndkr/lanarky: The web framework for building LLM microservices - GitHub Enhancing End-of-Life Management in LLM-Powered AI: The Key … - Medium
Learn more
1
github.com
2
medi
