Understand artificial intelligence for the Web
AI vocabulary often mixes model capability, access to documents and ability to act through tools. Separate these dimensions. A fluent answer can be incorrect; a connected tool can perform an action without the model understanding every consequence.
32 concepts to explore with their definitions and practical advice.
Search the full glossary
A reading path
1. Define the task
Specify question, allowed sources and expected result. A prompt cannot replace missing information or an undefined business rule.
2. Understand context and tools
Document retrieval adds context; a tool protocol organises exchanges with applications. Define separately what the system can read and what it can change.
3. Evaluate difficult cases
Keep authorised examples and success criteria. Test missing sources, conflicting information and out-of-scope requests before deploying the journey.
Useful distinctions
Retrieval-Augmented Generation / Model Context Protocol
RAG names generation grounded in retrieved information. MCP organises access to compatible resources and tools. Neither automatically requires the other, and neither guarantees accuracy.
Chatbot / AI agent
A chatbot is a conversational interface. An agent can receive goals and use tools within its scope. The agent label does not remove the need to define permissions, limits and action controls.
Use the vocabulary to make a decision
Situation
An assistant gives a convincing answer whose links do not support its facts.
Before deciding
Check claims individually against authorised documents. A displayed citation does not establish that the source actually says what is claimed.
A concrete check
Include this case in the evaluation set and plan an explicit response when information cannot be confirmed.
All concepts in this topic
Links open the full definition on its alphabet page. Acronyms and synonyms remain searchable from the main glossary.
- Agentic commerce
- Agentic Web
- AI Act
- AI agent
- AI citation
- AI crawler
- AI evaluation
- AI-assisted search
- Algorithmic transparency
- Artificial intelligence
- Chatbot
- Chunking
- Embedding
- Generative AI
- Grounding
- Hallucination
- LLM
- Machine learning
- Model Context Protocol
- Model routing
- Multimodal model
- Natural language processing
- Neural network
- OAI-SearchBot
- Prompt
- Prompt engineering
- Retrieval-Augmented Generation
- Token
- Transformer
- Vector database
- Vector search
- Web agent
Frequently asked questions
Does Web access eliminate errors?
No. It can provide new information, but selection, reading and interpretation still require checking. Keep the primary source and context.
Should tools be connected for the first trial?
Start with capabilities required by the task. Add a tool when usefulness and limits are defined, then evaluate parameter errors and out-of-scope requests.
Primary reference documents
These documents explain the technical concepts. The reading paths and decision examples are editorial methods to adapt to your project.