We want to empower you to experiment with LLM models, build your own applications, and discover untapped problem spaces. That’s why we sat down with GitHub’s Alireza Goudarzi, a senior machine learning researcher, and Albert Ziegler, a principal machine learning engineer, to discuss the emerging architecture of today’s LLMs.

In this post, we’ll cover five major steps to building your own LLM app, the emerging architecture of today’s LLM apps, and problem areas that you can start exploring today.

Five steps to building an LLM app

Building software with LLMs, or any machine learning (ML) model, is fundamentally different from building software without them. For one, rather than compiling source code into binary to run a series of commands, developers need to navigate datasets, embeddings, and parameter weights to generate consistent and accurate outputs. After all, LLM outputs are probabilistic and don’t produce the same predictable outcomes.

Click on diagram to enlarge and save.Let’s break down, at a high level, the steps to build an LLM app today.

The emerging architecture of LLM apps

Let’s get started on architecture. We’re going to revisit our friend Dave, whose Wi-Fi went out on the day of his World Cup watch party. Fortunately, Dave was able to get his Wi-Fi running in time for the game, thanks to an LLM-powered assistant.

Click diagram to enlarge and save.We’ll use this example and the diagram above to walk through a user flow with an LLM app, and break down the kinds of tools you’d need to build it.

// pay attention to the the following relevant information.
to the colors and blinking pattern.

// pay attention to the following relevant information.

// The following is an IT complaint from, Dave Anderson, IT support expert.
Answers to Dave's questions should serve as an example of the excellent support
provided by the ISP to its customers.

*Dave: Oh it's awful! This is the big game day. My TV was connected to my
Wi-Fi, but I bumped the counter and the Wi-Fi box fell off and broke! Now we
can't watch the game.

---

Real-world impact of LLMs

Looking for inspiration or a problem space to start exploring? Here’s a list of ongoing projects where LLM apps and models are making real-world impact.

• NASA and IBM recently open sourced the largest geospatial AI model to increase access to NASA earth science data. The hope is to accelerate discovery and understanding of climate effects.
• Read how the Johns Hopkins Applied Physics Laboratory is designing a conversational AI agent that provides, in plain English, medical guidance to untrained soldiers in the field based on established care procedures.
• Companies like Duolingo and Mercado Libre are using GitHub Copilot to help more people learn another language (for free) and democratize ecommerce in Latin America, respectively.

Further reading