The following plugins are available for LLM. Here’s how to install them.

For the past 6 months, I’ve been working on LLM-powered applications using GPT and other AI-as-a-Service providers. Along the way, I produced a set of illustrations to help visualize and explain some general architectural concepts.

Below is the first batch, I’m hoping to add more later on.

Building LLM-powered Applications

The past few weeks have been exciting for developers interested in deploying AI-powered applications. The field is evolving quickly, and it is now possible to build AI-powered applications without having to spend months or years learning the ins and outs of machine learning. This opens up a whole new world of possibilities, as developers can now experiment with AI in ways that were never before possible.

How can you leverage the capabilities of Large Language Models (LLMs) within your software applications?

You cannot simply create a thin application layer above an LLM API. Instead you need to design and build a number of components to ‘tame’ the underlying models and also to differentiate your product.

Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. Often, LLMs need to interact with other software, databases, or APIs to accomplish complex tasks. For example, an administrative chatbot that schedules meetings would require access to employees’ calendars and email. With access to tools, LLM agents can become more powerful—at the cost of additional complexity.

🚀 People ask me what it takes to build a scalable Large Language Model (LLM) app in 2023. When we talk about scalable we mean 100s of users and millisecond latency. Let me share some of our learning experience with you.