Engineering

Real-time language translation has become essential for global applications, communities, and businesses to break down language barriers and foster inclusive environments. In this article, I’ll explain how to implement real-time language translation in Stream applications using large language models (LLMs). By the end, you will understand how to: Authenticate users using a Stream token. Create

AI chatbots have become a familiar feature in many modern applications. Yet, practical questions remain: “How should we integrate a chatbot into our service?” To explore this question, I launched an experimental project: a sommelier chatbot. Wine is a domain rich with variety and unfamiliar terms—perfect for testing how helpful an AI assistant can be

The OpenAI Agents SDK for Python provides developers with the building blocks to implement two agentic solutions for AI applications. You can create text-generation agents, allowing users to get responses from text prompts. Additionally, you can build voice agents using the SDK. To create your first agent with the OpenAI Agents SDK, get started here.

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

Internet-based communication has become the backbone of business operations, personal interactions, and emergency services. Technologies like Voice over Internet Protocol (VoIP) and Web Real-Time Communication (WebRTC) have revolutionized communication, offering flexible and cost-effective alternatives to traditional telephony. However, regulatory challenges in some countries pose strict restrictions on VoIP services. This article explores the differences between

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

Your AI chatbot is up and running. It’s helping customers, getting them the information they need in the tone and manner that is right for your brand. CX costs are down, and your support team are moving up the value chain. Everyone is happy. And then it happens: spam. Automated bots flood your system with

Toolkits for AI agents expose developers to various APIs to equip AI solutions with tools to carry out tasks and ensure accurate results for user satisfaction. However, integrating these tools into AI apps and managing them can be messy. This article introduces you to an industry standard of providing context to LLMs and agents using