AI chatbots are software products that use artificial intelligence, primarily Natural Language Understanding (NLU) and Large Language Models (LLM) to conduct human-like, conversational interactions via text or voice. Unlike scripted or purely rule-based bots, AI chatbots infer user intent, maintain context, and generate or retrieve responses dynamically.
Central to the functioning of AI chatbots is the use of advanced machine learning (ML) models known as LLMs. They use patterns in the data they have been trained on to predict the most appropriate response. The sophistication of these LLMs sets AI
chatbots apart from their predecessors, enabling more nuanced understanding and generation of language. It also supports multi-turn reasoning and the use of Retrieval-Augmented Generation (RAG), tool/API calls, structured prompts, or lightweight in-session flows to complete the current request.
Although similar to other automation and productivity solutions, AI chatbots differ in critical ways. As opposed to chatbot software and productivity bots software, AI chatbots are product solutions that can operate independently and/or be embedded via widgets, Software Development Kits (SDKs), or integrations. That being said, some solutions allow for connections and integrations to proprietary business data and systems.
To qualify for inclusion in the AI Chatbot category, a product must:
Provide a conversational user interface that is configurable via system prompts, fine-tuning, and knowledge-base settings to improve accuracy while preserving human-like dialogue
Use a turn-based, conversation-first interface where interactions are primarily user-initiated
Restrict proactive behavior to in-session tool calls with explicit user confirmation; no background, open-ended, or cross-session autonomous activity
Provide a conversation-first interface for broad assistance across domains
Optimize for conversational assistance on simple to moderately complex tasks (e.g., information retrieval, drafting, summarization, structured Q&A, and light workflow orchestration via tools)
Operate in a controlled environment with governed access to knowledge bases, model context protocols (MCPs), and integrations to ensure reliable, auditable responses
Be powered by either stochastic generative models (e.g., LLMs) or intent-based NLU pipelines that select or generate responses during the session
Refrain from autonomous operation outside the active conversation (e.g., no unattended execution, agents continuing after session end, or actions without explicit, in-session approval)
Support configurable rules, scripts, or decision trees to constrain and guide conversations even when LLMs are used to ensure predictable, policy-compliant flow
