33 AI terms explained in plain language, from Agentic AI to Zero-shot learning.
Autonomous AI systems that can reason, plan, and execute multi-step tasks independently. Unlike traditional chatbots, agentic AI takes initiative, uses tools, makes decisions, and adapts its approach based on results—all with minimal human oversight.
Example: An agentic AI system that researches competitors, analyzes data, and generates a market report without step-by-step human guidance.
An AI system that can perceive its environment, make decisions, and take actions to achieve specific goals. Agents typically use tools (APIs, databases) and can operate semi-autonomously within defined boundaries.
Example: A sales AI agent that accesses your CRM, researches leads, and drafts personalized outreach emails.
A set of protocols that allows different software applications to communicate with each other. AI systems use APIs to access external services, data, and capabilities.
Example: A chatbot uses the OpenAI API to generate responses and a Stripe API to process payments.
A technique in neural networks that allows models to focus on relevant parts of the input when generating output. It's the core innovation behind transformers and modern LLMs.
AI systems capable of operating independently without continuous human input. They can set sub-goals, execute plans, and adjust behavior based on outcomes.
Optimization strategies for appearing in AI-generated answers and voice search results. Focuses on structured data, direct answers, and FAQ content that AI systems can easily extract.
Coordinating multiple AI agents, models, or services to work together on complex tasks. Includes routing, sequencing, and managing handoffs between components.
Example: A supervisor agent that routes customer requests to specialized sub-agents for billing, technical support, or sales.
The ability of AI systems to draw conclusions, make inferences, and solve problems through logical thinking. Advanced reasoning is key to agentic AI capabilities.
A conversational AI interface that responds to user inputs through text or voice. Modern chatbots use LLMs for natural language understanding and can handle complex conversations.
Example: A customer support chatbot that answers FAQs, collects information, and routes complex issues to human agents.
A prompting technique that encourages AI models to break down complex problems into step-by-step reasoning, improving accuracy on tasks requiring logic or math.
Example: Asking an LLM to "think step by step" before solving a math problem.
The maximum amount of text (measured in tokens) that an AI model can process at once. Larger context windows allow for longer conversations and more document analysis.
Example: GPT-4 Turbo has a 128K token context window, allowing it to process ~300 pages of text.
AI systems designed for natural language dialogue with humans. Includes chatbots, voice assistants, and interactive agents that can understand context and maintain coherent conversations.
Numerical vector representations of text, images, or other data that capture semantic meaning. Similar concepts have similar embeddings, enabling search and comparison.
Example: "king" and "queen" have similar embeddings because they share semantic relationships.
Google's quality guidelines for evaluating content. Important for SEO and increasingly relevant for AI search systems that prioritize credible sources.
Training a pre-trained AI model on specific data to adapt it for particular tasks or domains. Creates specialized models without training from scratch.
Example: Fine-tuning GPT on medical literature to create a healthcare-specialized assistant.
The ability of LLMs to invoke external functions or APIs based on conversation context. Enables AI to take actions like querying databases, sending emails, or executing code.
Optimization strategies for visibility in AI-powered search engines like ChatGPT, Perplexity, and Google AI Overviews. Focuses on being cited as a source by AI systems.
AI systems that create new content (text, images, code, audio) rather than just analyzing existing data. Includes LLMs like GPT and image generators like DALL-E.
Connecting AI responses to factual, verifiable information sources. Reduces hallucinations by ensuring outputs are based on retrieved documents or knowledge bases.
When an AI model generates plausible-sounding but factually incorrect or fabricated information. A key challenge in deploying LLMs for business applications.
Example: An AI confidently citing a non-existent research paper or inventing statistics.
AI systems that include human oversight or intervention at critical decision points. Balances automation with human judgment for sensitive or high-stakes actions.
Example: An AI agent that drafts emails but requires human approval before sending.
AI models trained on vast amounts of text data that can understand and generate human-like text. The foundation of modern chatbots, assistants, and AI agents.
Examples: GPT-4 (OpenAI), Claude (Anthropic), Gemini (Google), Llama (Meta)
A popular open-source framework for building applications with LLMs. Provides tools for prompt management, memory, agents, and integrations.
Architectures where multiple AI agents collaborate or compete to solve complex problems. Each agent may have specialized roles, tools, or expertise.
Example: A research agent, writing agent, and review agent working together to produce a report.
Anthropic's open standard for connecting AI assistants to external data sources and tools. Enables secure, standardized integrations across different AI systems.
The practice of crafting effective instructions (prompts) to get optimal outputs from AI models. Includes techniques like few-shot learning, chain-of-thought, and role-playing.
A security vulnerability where malicious input tricks an AI into ignoring its instructions or performing unintended actions. A key concern for production AI systems.
A technique that enhances LLM responses by retrieving relevant information from external knowledge bases before generating answers. Reduces hallucinations and enables domain-specific responses.
Example: A support chatbot that retrieves relevant documentation before answering customer questions.
The basic unit of text processed by LLMs. Roughly 4 characters or 0.75 words in English. API pricing and context limits are measured in tokens.
Example: "Hello world" is 2 tokens. A 1000-word document is roughly 1,300 tokens.
The neural network architecture behind modern LLMs. Uses attention mechanisms to process sequential data in parallel, enabling training on massive datasets.
The ability of AI agents to invoke external tools, APIs, or functions to accomplish tasks beyond text generation. Essential for agents that take real-world actions.
Specialized databases designed to store and query embedding vectors efficiently. Essential for RAG systems and semantic search applications.
Examples: Pinecone, Weaviate, Chroma, Qdrant, pgvector
The ability of AI models to perform tasks without being explicitly trained on examples of that task. Modern LLMs can handle many tasks zero-shot through prompt instructions.
Example: Asking an LLM to translate text without providing translation examples.
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