Chatbot
Software that engages in natural language conversations with users via text or voice. Recently, it has evolved beyond simple rule-based responses into intelligent assistants that understand complex context and perform professional tasks by combining Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technologies.
Detailed explanation
Why it matters in tool selection
Chatbots not only reduce customer service costs by up to 30% but also serve as key tools to boost customer satisfaction through 24/7 real-time support. Recently, their value as 'knowledge workers' utilizing a company's internal knowledge has grown beyond simple Q&A capabilities. Therefore, when adopting a chatbot, it is essential to consider security and data integration capabilities beyond basic conversational skills.
Key considerations
- Does it support Retrieval-Augmented Generation (RAG) technology to prevent hallucination?
- Can it integrate via API with existing work tools (CRM, ERP, Slack, etc.)?
- Does it have a security policy that excludes user input data from model training?
- How long and accurately can it maintain conversational context (memory)?
Use cases
Traditional customer service chatbots (such as Zendesk AI) perform standardized customer responses, while generative chatbots (like ChatGPT and Claude) assist with complex document summarization and creative writing. Search-based chatbots like Perplexity summarize up-to-date web information in real-time, providing sources.
Easily confused terms
Rule-Based Chatbots
Respond only to pre-defined buttons or keywords and cannot answer questions outside of the set scenario.
AI Agents
Unlike chatbots that only converse, they set goals on their own and use various tools to directly execute tasks (e.g., sending emails, processing payments, etc.).