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Plain-language AI glossary

Term 49ApplicationsMeaning / context / connections

Applications / Definition

Chatbot

An AI application that conducts conversations with users through text or voice, handling questions, tasks, and interactions in natural language.

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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Chatbot works.

Chatbots are the most common AI application. Modern chatbots powered by LLMs are fundamentally different from the rule-based chatbots of the past. Instead of matching keywords to scripted responses, they understand context, handle follow-up questions, and generate natural responses. Building one requires: an LLM API (OpenAI, Anthropic, or Google), conversation history management, and a user interface.

The technical architecture: (1) User sends a message, (2) Your app prepends a system prompt (defining the chatbot's personality and rules), (3) Previous messages are included for context, (4) The LLM generates a response, (5) The response is streamed back to the user. Use OpenAI's Conversations API or maintain your own message history. Add RAG for knowledge-grounded responses and function calling for action-taking capability.

Production chatbot considerations: system prompt design (defines quality), conversation length management (truncate or summarize old messages to stay within context limits), safety filters (prevent prompt injection and harmful outputs), fallback handling (escalate to humans when the AI cannot help), and analytics (track conversation quality, user satisfaction, and common failure modes).

02 / Practical uses

Where it helps.

  1. 01Customer support automation
  2. 02Product recommendations
  3. 03FAQ and knowledge base access
  4. 04Onboarding and guided experiences
  5. 05Personal AI assistants
03 / Keep exploring
Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.StreamingA method of receiving AI model output token-by-token in real time as it is generated, rather than waiting for the complete response.RAG (Retrieval-Augmented Generation)A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.Prompt EngineeringThe practice of crafting effective instructions for AI models to produce desired outputs consistently.