Bing Chat

Generates natural language responses to user queries by integrating real-time web search results into the conversation context. Uses a retrieval-augmented generation (RAG) pattern where Bing's search index provides current information that is then synthesized by the underlying language model into coherent, cited responses. The system maintains conversation history to enable multi-turn dialogue while anchoring responses to web sources rather than relying solely on training data.

Maintains and manages conversation history across multiple turns, allowing the model to reference previous messages, build on prior context, and handle clarifications or follow-ups. The system stores conversation state (user messages, assistant responses, and implicit context) and uses this history to inform subsequent generations, enabling coherent multi-step reasoning and topic continuity without requiring users to re-specify context.

Generates code snippets and technical explanations by combining the language model's code generation capabilities with real-time web search for current libraries, frameworks, and best practices. When users ask for code solutions, the system retrieves relevant documentation, Stack Overflow answers, and GitHub examples from the web, then synthesizes these into generated code with explanations and source citations.

Analyzes images uploaded by users and answers questions about their content, including object detection, scene understanding, text extraction (OCR), and visual reasoning. The system processes image inputs through a multimodal model that combines vision and language understanding, then generates natural language descriptions or answers based on the visual content.

Translates natural language questions into effective search queries and retrieves relevant information from Bing's index, then synthesizes results into conversational responses. Unlike traditional search engines that return ranked links, this capability interprets user intent, performs the search, and generates a natural language answer that directly addresses the question.

Allows users to specify desired tone, formality level, and response style (e.g., 'creative', 'balanced', 'precise') which influences how the model generates responses. The system uses these preferences as control signals during generation, adjusting vocabulary, sentence structure, and emphasis to match the requested style while maintaining factual accuracy.

Evaluates claims in responses against web sources and flags potentially inaccurate information. When generating responses, the system cross-references assertions with search results and can highlight claims that lack strong source support or contradict available information. This is implemented through a verification layer that checks generated statements against retrieved web content.

Allows users to export conversations in multiple formats (text, markdown, PDF) and share them with others via links or direct download. The system serializes conversation history including user messages, assistant responses, and citations, then formats it for external consumption or sharing.