While chatbots are a dominant use case for LLMs, the underlying technology is incredibly versatile for various text generation tasks. "Completions" refer to the model generating text that follows a given prompt, without necessarily being part of an ongoing conversation.
How to design and implement APIs that leverage AI capabilities.
In Part 3, we explored basic text completions. Now, let's take it a step further. "Text Generation" can encompass a wide range of tasks, from writing creative stories and poems to generating code, marketing copy, or detailed explanations. The key to unlocking these capabilities often lies in prompt engineering – the art and science of crafting effective prompts to guide the LLM.
In Part 1, we built a basic chatbot. While impressive, its capabilities were limited to the knowledge baked into the LLM. To make our chatbot truly powerful, we need to give it the ability to interact with the outside world or perform specific actions. This is where "tools" (often referred to as function calling) come in.
Welcome to our deep-dive series on the Vercel AI SDK! In a world increasingly powered by artificial intelligence, developers need robust, easy-to-use tools to integrate AI capabilities into their applications. The Vercel AI SDK is a powerful, open-source library designed to help you build AI-powered user interfaces with your favorite JavaScript frameworks.
In previous parts, we've seen how the Vercel AI SDK can generate and stream text. But what if you need the AI to output data in a specific, structured format, like JSON?
This playbook guides you through using a Large Language Model (LLM) assistant to generate a solution architecture diagram following the Archimate specification. It covers framing your prompt, getting the output in PlantUML language, and rendering it with a PlantUML editor.