# chatbots Published articles for chatbots. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI Chatbot Trained on Your WordPress Content: Setup Guide (RAG Explained) DevFeed: [AI Chatbot Trained on Your WordPress Content: Setup Guide (RAG Explained)]() Original publisher: [Read original article]() Author: Team WPShout Published: 2026-08-14T10:54:34Z Content type: tutorial Language: en Sources: [Back-End Development - WPShout]() Topics: [Retrieval Augmented Generation (RAG)](), [WordPress AI](), [AI Chat](), [WordPress](), [API](), [API keys](), [Open Source](), [PHP](), [GitHub](), [OpenAI]() Tags: [ai](), [api](), [back-end-development](), [chatbots](), [code](), [github](), [learn](), [open-source](), [openai](), [php](), [rag](), [wordpress](), [wordpress-ai](), [wordpress-plugins]() ### AI overview A guide explains retrieval-augmented generation (RAG) and shows how to set up the free Hyve Lite WordPress AI chatbot to answer questions using site posts and pages. It also describes prerequisites, API-key options, and limitations. ### Source excerpt Want a WordPress AI chatbot that answers from your own posts and pages? Learn how RAG actually works, then set up Hyve Lite free, limits and all. ## How novice coders can develop AI programs for military applications DevFeed: [How novice coders can develop AI programs for military applications]() Original publisher: [Read original article]() Author: Haley Wahl | MIT Lincoln Laboratory Published: 2026-07-07T17:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Vibe coding](), [AI Development](), [AI Chat](), [Language models](), [Software Engineering](), [Development]() Tags: [ai](), [ai-and-military](), [ai-assisted-target-recognition](), [ai-chatbots](), [ai-programming](), [ai-writing-software](), [artificial-intelligence](), [autonomous-striking](), [battlefield-communications](), [chatbots](), [chatgpt](), [claude](), [coding](), [computer-science-and-technology](), [daf-mit-ai-accelerator-phantom-program](), [gemini-llm](), [google-ai-studio-app](), [language-models](), [laura-niss](), [lincoln-laboratory](), [military-ai](), [mit-lincoln-laboratory](), [novice-coders](), [programming](), [remote-operating-modular-augmentation-device-romad-ai](), [research](), [security-and-military-studies](), [software](), [software-development](), [u-s-armed-forces](), [vibe-coding](), [vision-language-models-vlms]() ### AI overview A U.S. Air Force cadet with no coding experience used generative AI chatbots and a process called vibe-coding to develop a military application. The project examined whether nontechnical service members could create useful software for military problems. ### Source excerpt A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems. ## What Is Software, and Will LLMs Replace It? DevFeed: [What Is Software, and Will LLMs Replace It?]() Original publisher: [Read original article]() Author: Federico Tomassetti Published: 2026-06-23T08:56:06Z Content type: opinion Language: en Sources: [Federico Tomassetti]() Topics: [Large Language Model](), [Software](), [data](), [integrity](), [Software as a service]() Tags: [ai](), [business](), [chatbots](), [consistency](), [data](), [future-of-ai](), [future-of-software](), [integrity](), [interfaces](), [language-engineering](), [llms](), [mcp](), [opinion](), [patterns](), [reflections](), [saas](), [software](), [software-development](), [sql](), [structure](), [visualization]() ### AI overview The article argues that large language models are unlikely to replace software. Instead, they may provide more flexible interfaces while software's underlying structures--organized data, schemas, constraints, consistency rules, visualizations, and guided processes--remain essential. ### Source excerpt Software isn't being replaced by LLMs, it's being fronted by them, with the deterministic core (schemas, constraints, processes) staying as essential as ever. The post What Is Software, and Will LLMs Replace It? appeared first on Federico Tomassetti. ## Trust Is the Currency, Knowledge Is the Engine DevFeed: [Trust Is the Currency, Knowledge Is the Engine]() Original publisher: [Read original article]() Author: UX Magazine Team Published: 2026-05-26T09:18:36Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [Artificial Intelligence](), [data]() Tags: [agent](), [ai](), [chatbots](), [enterprise](), [mental-models](), [training](), [use-cases](), [workflow]() ### AI overview Mastercard's central AI and data team receives roughly a thousand AI requests each year, with growing interest in agents rather than chatbots. The article argues that organizations should build AI knowledge and fluency before deployment, because polished demonstrations may conceal serious engineering weaknesses. It also describes a simple framework for prioritizing AI use cases around more secure, smarter, and more personal commerce and a stronger Mastercard. ### Source excerpt What Mastercard's AI lead understands about enterprise transformation that most organizations are still missing. Federico Cohen Freue fields roughly a thousand AI requests a year. That's the incoming volume to Mastercard's central AI and data team -- proposals, ideas, and asks from across a global enterprise trying to figure out where and how to deploy The post Trust Is the Currency, Knowledge Is the Engine appeared first on UX Magazine. ## Making the Invisible, Visible: 6 Months of Diving Deeper into AI DevFeed: [Making the Invisible, Visible: 6 Months of Diving Deeper into AI]() Original publisher: [Read original article]() Author: Anina Botha Published: 2026-05-21T07:03:08Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [User experience (UX)](), [Artificial Intelligence]() Tags: [academic-research](), [ai](), [chatbots](), [ux]() ### AI overview A UX practitioner reflects on six months of studying AI, human behavior, and academic research to develop principles for product teams. The article focuses on designing better human-technology experiences, including appropriate trust, adoption, chatbots, and prompts. ### Source excerpt What I didn't do I have not vibe-coded, crafted the perfect prompt, created a skill to use, or [insert any trending thing right now]. Not because I'm not interested -- believe me, it's tempting. But my focus has been elsewhere. It simply hasn't aligned with my goals and needs. Not yet. For the last 6 The post Making the Invisible, Visible: 6 Months of Diving Deeper into AI appeared first on UX Magazine. ## Ask AIMee: An accessible accessibility-focused AI chatbot DevFeed: [Ask AIMee: An accessible accessibility-focused AI chatbot]() Original publisher: [Read original article]() Author: Jared Smith Published: 2026-03-31T16:49:07Z Content type: release Language: en Sources: [WebAIM Blog]() Topics: [Accessibility](), [Artificial Intelligence](), [Chat Bot](), [Large Language Model](), [qwen](), [Code]() Tags: [accessibility](), [ai](), [chat](), [chatbots](), [hallucinations](), [llm](), [qwen](), [uncategorized]() ### AI overview WebAIM introduces AIMee, an AI-powered conversational chatbot focused on accessibility. It is designed to support users with disabilities by answering accessibility questions, reviewing code, drafting policies, explaining concepts, and generating resources, checklists, and quizzes. AIMee primarily uses the Qwen 3 Coder large language model with additional safeguards, but its answers can still contain hallucinations and should be verified. ### Source excerpt We're happy to introduce AIMee - an easy-to-use, AI-powered conversational chatbot focused on accessibility. AIMee has been designed to be highly accessible to users with disabilities. Ask her accessibility questions to get quick answers and guidance. The name "AIMee" plays off of the "AIM" (Accessibility In Mind) from "WebAIM" and also "AI". Here are some [...] ## A differentially private framework for gaining insights into AI chatbot use DevFeed: [A differentially private framework for gaining insights into AI chatbot use]() Original publisher: [Read original article]() Published: 2025-12-10T21:59:41Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [AI Chat](), [Large Language Model](), [Google](), [Algorithm](), [pii]() Tags: [ai](), [chatbots](), [clustering](), [code](), [data](), [data-protection](), [generative-ai](), [google](), [insights](), [large-language-model](), [llm](), [pii](), [pipeline](), [research](), [responsible-ai](), [security-privacy-and-abuse-prevention]() ### AI overview Google Research introduces Urania, a framework for generating high-level insights into AI chatbot usage while protecting user conversation privacy. Its pipeline combines differentially private clustering, keyword extraction, and LLM summarization to provide formal, end-to-end differential privacy guarantees. ### Source excerpt Generative AI ## AI progress and recommendations DevFeed: [AI progress and recommendations]() Original publisher: [Read original article]() Published: 2025-11-06T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [AI Strategy](), [Software Engineering]() Tags: [2026](), [ai](), [chatbots](), [company](), [cost](), [discovery](), [future](), [recommendations](), [research](), [safety]() ### AI overview The article describes rapid advances in AI, including systems that can outperform top human performance in difficult intellectual competitions and increasingly support software engineering and knowledge discovery. It discusses falling costs, expected capability gains through 2026 and beyond, societal adaptation, safety, and the possibility of a better future. ### Source excerpt AI is advancing fast. We have the chance to shape its progress--toward discovery, safety, and a better future for everyone. ## Evolution of the Udemy AI Assistant Intent Understanding System DevFeed: [Evolution of the Udemy AI Assistant Intent Understanding System]() Original publisher: [Read original article]() Author: Jack Kwok Published: 2025-05-28T16:35:59Z Content type: article Language: en Sources: [Udemy Engineering]() Topics: [Artificial Intelligence](), [Language models](), [context](), [API](), [Development]() Tags: [ai](), [api](), [artificial-intelligence](), [chat](), [chatbots](), [context](), [cost](), [development](), [function](), [generation](), [language](), [language-models](), [llm](), [llms](), [models](), [quality](), [responses](), [search](), [speed](), [udemy]() ### AI overview This article describes the evolution of Udemy's AI Assistant intent-understanding system. It explains how the system predicts a learner's intent and invokes chains of actions to retrieve relevant context from data sources and APIs, including lecture summaries and course search, while balancing response quality, speed, and cost. ### Source excerpt Introduction Imagine having a virtual learning companion by your side every time you tackle a new course or challenging concept, ready to clarify doubts, summarize lessons, or quiz your understanding on demand. This isn't just an idea for the future. Thanks to the Udemy AI Assistant, it's a reality for every Udemy learner today. Powered by AI, the Assistant is actively supporting learners all over the world, guiding them step by step through their skills development journeys and making learning smoother, faster, and more personalized than ever. Behind the scenes, delivering such smart, relevant help relies on one crucial ability: understanding exactly what the learner wants. From course-related clarifications to lecture summaries and beyond, the AI Assistant must accurately grasp the intent behind every question to deliver the perfect response. In this article, we'll take you behind the curtain to explore how we've evolved the Udemy AI Assistant's intent understanding system. You'll discover key lessons from our journey: the challenges we faced as we expanded features, breakthroughs that boosted accuracy, and how we balanced response quality, speed, and cost. Background With Large Language Models (LLMs), setting the relevant context in the prompt is key to eliciting high-quality, high-relevance chat responses. To that end, we enable the AI Assistant to connect to various data sources and services to obtain the most relevant context. Behind the scenes, the AI Assistant predicts the learner's intent and then invokes the corresponding chain of actions to fetch the most helpful context from data sources and APIs. For example, if the learner intends to ask for a summary of the current video lecture, a chain of actions would include invoking an API call to fetch the pre-generated lecture summary and send it to the LLM for final response generation. As another example, if the learner intends to search for a topic within the course, a function call will execute a search t ## How to Make a RAG Application With LangChain4j DevFeed: [How to Make a RAG Application With LangChain4j]() Original publisher: [Read original article]() Author: Tim Kelly Published: 2025-05-03T12:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [Retrieval-Augmented Generation](), [Databases](), [Development](), [Embeddings](), [Java](), [Library](), [MongoDB](), [AI Development](), [Chat Bot]() Tags: [ai](), [artificial-intelligence](), [chatbots](), [database](), [embeddings](), [how-to](), [java](), [langchain4j](), [library](), [mongodb](), [rag]() ### AI overview This tutorial explains how to build a retrieval-augmented generation Q&A chatbot with LangChain4j. It describes retrieving data from MongoDB Atlas, storing documents as vector embeddings, and using the retrieved data to augment LLM prompts. ### Source excerpt Retrieval-augmented generation, or RAG, introduces some serious capabilities to your large language models (LLMs). These applications can answer questions about your specific corpus of knowledge, whil... The post How to Make a RAG Application With LangChain4j appeared first on DEV. ## How good are LLMs at fixing their mistakes? A chatbot arena experiment with Keras and TPUs DevFeed: [How good are LLMs at fixing their mistakes? A chatbot arena experiment with Keras and TPUs]() Original publisher: [Read original article]() Author: Martin Görner Published: 2024-12-05T00:00:00Z Content type: opinion Language: en Sources: [Hugging Face - Blog]() Topics: [Large Language Model](), [Chat Bot](), [gradio]() Tags: [chatbots](), [gradio](), [hallucinations](), [keras](), [llm](), [llms](), [open-source](), [spaces](), [tpu]() ### AI overview The author describes a small experiment testing whether LLMs can correct code-generation mistakes after receiving feedback in plain English. The setup uses a mobile-assistant prompt that requires single-line executable Python API calls and compares conversations with multiple chatbots in a Gradio interface. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Ep. 7: Enhancing AI with Message Chaining and Accuracy Scoring DevFeed: [Ep. 7: Enhancing AI with Message Chaining and Accuracy Scoring]() Original publisher: [Read original article]() Published: 2024-09-19T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Artificial Intelligence](), [Generative AI](), [context](), [AI Chat]() Tags: [advanced-ai-workflows](), [ai](), [ai-accuracy-scoring](), [ai-chatbot-continuity](), [ai-context-management](), [ai-conversation-tracking](), [ai-decision-making-validation](), [ai-driven-system-accuracy](), [ai-for-healthcare-accuracy](), [ai-legal-and-finance-accuracy](), [ai-message-chaining](), [ai-model-chaining](), [building-smarter-ai-systems](), [chatbots](), [complex-ai-tasks](), [context](), [context-aware-ai-systems](), [enhancing-ai-conversations](), [factuality-scoring-in-ai](), [generative-ai](), [generative-ai-techniques](), [intro-to-generative-ai-series](), [models](), [multi-faceted-ai-automation](), [multi-step-ai-processes](), [processes](), [reliable-ai-outputs](), [workflows]() ### AI overview This episode explains how message chaining preserves conversation context, how factuality scoring checks AI responses against verified sources, and how chaining multiple models enables complex, multi-step workflows. ### Source excerpt Introduction: Welcome to the final episode of our Intro to Generative AI series! In this episode, Daniel Whitenack takes the concepts you've been learning and shows you how to apply advanced techniques like message chaining and factuality scoring to make your AI-driven systems smarter and more reliable. This session will help you understand how to create workflows that combine multiple models, ensuring your AI can provide accurate, context-aware responses and make decisions grounded in real data. ## Supercharge your apps with Firebase and the Gemini API DevFeed: [Supercharge your apps with Firebase and the Gemini API]() Original publisher: [Read original article]() Author: Alexander Nohe; Frank van Puffelen Published: 2024-02-15T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Generative AI](), [Google AI](), [Firestore](), [Chat Bot](), [Image]() Tags: [ai](), [ai-studio](), [alt-text](), [chatbots](), [extensions](), [firebase](), [firestore](), [gemini](), [generative-ai](), [how-to](), [learn](), [web]() ### AI overview This tutorial explains how Firebase Extensions can integrate the Gemini API into applications. It demonstrates building a conference-session chatbot and automatically generating accessible alt text for images, with data stored in Firestore and behavior guided by prompts. ### Source excerpt Learn how to easily integrate Google's powerful Gemini into your Firebase apps. Build conversational chatbots and auto-generate image alt text with code-free simplicity. ## The Developer Show: Building Zoom Chatbots DevFeed: [The Developer Show: Building Zoom Chatbots]() Original publisher: [Read original article]() Author: Shrijana Ghimire Published: 2020-04-03T20:13:34Z Content type: article Language: en Sources: [Zoom Developer Blog - Medium]() Topics: [Chat Bot](), [zoom](), [Demo]() Tags: [advocacy](), [building](), [chatbots](), [communication](), [developer](), [developer-webinars](), [education](), [learning](), [support](), [tools](), [webinar](), [zoom](), [zoom-developer-show]() ### AI overview The Zoom Developer Advocacy team announced a free webinar series beginning April 9, 2020. The first webinar focuses on chatbots and includes Zoom chatbot tools, a marketplace app-building experience, and live and recorded demos. ### Source excerpt The Zoom Developer Advocacy team is obsessed with developer education and developer support. Up until now, we relied on our developer... Continue reading on Zoom Developer Blog " ## Debut of the Zoom Developer Show webinar -- Chat Apps! DevFeed: [Debut of the Zoom Developer Show webinar -- Chat Apps!]() Original publisher: [Read original article]() Author: Benjamin Dean Published: 2020-03-26T22:21:55Z Content type: news Language: en Sources: [Zoom Developer Blog - Medium]() Topics: [zoom](), [Chat Bot](), [Developer experience](), [Tool]() Tags: [apps](), [blog](), [chat](), [chatbots](), [developer](), [developer-experience](), [tool](), [webinar](), [zoom](), [zoom-developer-show]() ### AI overview The debut episode of the Zoom Developer Show is announced as a webinar featuring a discussion of DocketHQ's developer experience building a chatbot with Zoom, a demonstration of a new tool for building chatbots for Zoom Chat, and time for developer questions. ### Source excerpt Debut of the Zoom Developer Show webinar -- Chat Apps!The Zoom Developer Show! Join Michael Harrington and Shrijana Ghimire as they host the debut webinar episode of the Zoom Developer Show on April 9, 2020 at 11am PT! Welcome! You are invited to join a webinar: Zoom Developer Show - Chat Apps!. After registering, you will receive a confirmation email about joining the webinar. Dustin McCormick, CTO of DocketHQ, will be sharing his team's developer experience building a chatbot with Zoom. Tommy Gaessler and Zach Zhenggan will be unveiling a new developer tool for quickly building chatbots to work with Zoom Chat, and demoing it in action! We have time scheduled to field your developer-focused questions, and we'll have some fun too! Register Today: https://success.zoom.us/webinar/register/WN_iwEN8qFvSg-4z-XLle57fg Debut of the Zoom Developer Show webinar -- Chat Apps! was originally published in Zoom Developer Blog on Medium, where people are continuing the conversation by highlighting and responding to this story. ## Mobile apps are not doomed by chatbots and AI DevFeed: [Mobile apps are not doomed by chatbots and AI]() Original publisher: [Read original article]() Author: Wolfram Rittmeyer Published: 2018-02-27T09:11:55Z Content type: opinion Language: en Sources: [Grokking Android]() Topics: [Mobile](), [App](), [Bots](), [Artificial Intelligence](), [Bot]() Tags: [ai](), [android](), [apps](), [bots](), [chatbots](), [mobile]() ### AI overview This opinion article argues that headlines predicting the end of mobile apps because of chatbots, AI, or voice assistants are overstated. It suggests that apps and conversational interfaces will coexist, with each being more suitable for different user needs. ### Source excerpt So lately there are a lot of headlines appearing that say "Businesses Moving To Chatbots, AI; Away From Mobile Apps", "The Rise of Chatbots - Why they'll replace All Your Apps!" or "How Bots Will Completely Kill Websites and Mobile Apps". First of all: Look at where those headlines appear. Some of these websites - [...] Continue Reading "Those "mobile apps are doomed" headlines? They are wrong!" The post Those "mobile apps are doomed" headlines? They are wrong! appeared first on Grokking Android.