Tutorials: Chat

Hiring the right candidate and finding the perfect job can be challenging. Recruiters need an efficient way to manage job postings, screen applicants, and conduct virtual interviews—all in one platform. In this tutorial, you will learn how to build a job application and interviewing platform using Next.js, Stream, and Firebase. This app will allow recruiters

An interesting quirk of large language models (LLMs) is that they aren’t all the same. ChatGPT tends to be better for analysis, but in the words of Paul Graham, “writes like a kid doing an assignment.” Claude is a much better writer but loves a little bit of hallucination. All other models have their strong

In our previous article, we discussed setting up your stream chat application with advanced search features using Elasticsearch. In this article, we will combine this power tool with visualization software to enhance our chat application’s features and capabilities. (Note: This guide assumes some familiarity with Node.js, Docker, and basic command-line usage.) Combining Stream Chat, Elasticsearch,

Large Language Models, although trained on large amounts of data, have limited domain-specific knowledge. This limitation makes them less effective for customer support chatbots or similar apps that require specific data. Retrieval Augmented Generation (RAG) solves this concern by allowing LLMs access to external knowledge sources for a more accurate response generation. This means that

Note: This article assumes you have basic Node.js knowledge and are comfortable with environment variables. The code snippets in this article were run on a Windows OS. This GitHub repo contains all the files used in this article. Recently, applications or websites have been built with some form of chat. Whether it is through social

Chat applications have moved well beyond the basic IRC messaging they started as. With chat platforms such as Stream, you get access to a whole array of rich features: reactions, presence notifications, file uploads, and slash commands. These features change straightforward chat into meaningful real-time applications. But how do these work under the hood? By

Our aim today is straightforward. We learn how to integrate an AI assistant with Stream's Chat APIs and make it blazing fast, like Le Chat by Mistral AI, using the LLM inference platforms. After the February 2025 updates and improvements to Le Chat by Mistral, many developers and power users have compared its response speed

Deepseek is the latest LLM to hit the digital shelves. It boasts high-quality reasoning at a fraction of the cost of current state-of-the-art models, OpenAI o1 and o3-mini, and Gemini 2.0 Flash Thinking. DeepSeek R1 is open-source, which means two things. First, developers can examine the model's architecture, training process, and weights directly, enabling a