AI Glossary (WIP)
This is a short list of common AI terms and their definitions, compiled as I was becoming familiar with the basics.
Agent
AI system that pursues a goal autonomously, planning and completing complex, multi-step tasks. An agent is a model running inside a harness: it receives an end goal, figures out how to tackle the job, then works toward it step by step. In the observe-think-act loop, an agent can call tools and feed the results back in, so context builds across the loop rather than resetting each step.
AI Safety
Field for making AI systems beneficial and safe to use.
Artificial Intelligence (AI)
Development of computer systems that perform tasks typically requiring human intelligence. AI systems learn from data to make decisions, recognize patterns, and solve problems, instead of relying only on explicit, pre-programmed instructions.
Bias
Errors in output resulting from biased training data.
Computer Vision
AI interpreting images and video.
Context
The AI model’s working memory, the information it can access and use when generating a response. Context resets for each conversation, it’s not saved, nor is it training. The context window is the maximum amount of info that a model can consider at once, including prompt, conversation history, and response. Context is most effective at the beginning (primacy bias), then end (recency bias), of the session; context in the middle can get overlooked.
General AI (AGI)
Not-yet-achieved AI system with human-level reasoning across any domain.
Hallucination
When a model confidently generates false information.
Harness
Software scaffolding around a model that turns it into a working application. The model only generates text; the harness handles system prompt, context window, output parsing, and tool calls. When the harness runs an observe-think-act loop, deciding what to send the model next and what to do with each response, the result is an agent.
Inference
Using a model to generate responses in a chat.
Large Language Model (LLM)
AI trained on massive text datasets.
LLMs are a type of ML. ML is a type of AI.
Latency
How long the model takes to respond.
Machine Learning (ML)
Systems that learn patterns from data. Deep learning is a subset of ML using layered neural networks inspired by the brain.
LLMs are a type of ML. ML is a type of AI.
Model
Massive file of numbers (weights or parameters) that work together mathematically to generate text responses. The model has no memory, personality, or tools. It generates answers token by token based on probability.
Model Context Protocol (MCP)
Open standard that allows AI models to connect with external tools, data sources, and services. Allows plug-and-play for tools outside conversation with agent. Examples: search the web, access files, query databases, interact with app like Slack or Gmail.
Multi-Agent
Systems where multiple AI models collaborate on a task. Orchestration is coordinating multiple AI agents or steps to complete complex tasks.
Natural Language Processing (NLP)
AI understanding and generating human language.
Persona
Defined character, identity, or personality assigned to model to shape its tone, voice, and style of response. Overlaps with role prompting, but focuses on personality and communication style rather than expertise or task focus.
Prompt Engineering
Crafting inputs to get better AI outputs.
Standard prompting - asking direct question or giving direct instruction in a short prompt
Zero-shot prompting - giving the model instruction with high specificity and low complexity without examples
One-shot prompting - including a standard pattern example in the prompt with the format or style of desired output
Few-shot prompting - including a few diverse examples for complex cases, such as including a description, technical requirements, implementation and integration notes, and expected deliverable with data structure
Role prompting - assigning an identity or expertise for the tone or focus of the output
Chain of thought prompting - asking model to break down its reasoning into intermediate steps before giving a final answer (such as including “let’s think step by step” in a zero shot)
Emotional prompting - including emotional language or stakes to influence the model's response
Prompt chaining - breaking a complex task into separate prompts where each output feeds into the next
Negative prompting - specifying what you don’t want in the output
Meta-prompting - having model write or improve prompts for itself or another model
Retrieval Augmented Generation (RAG)
Giving LLM the ability to look up relevant information from an external source before generating a response. Allows to stay up-to-date without retraining.
Skill
Modular set of instructions or best practices that improve AI output for specific a task. A skill is a recipe that the model reads before starting a task. Examples: write text documents, work with PDFs, create UI components, write tests, review pull requests.
Structured Output
Model returns its response in a specific, predictable shape that matches a provided template, schema, or data format, rather than free-form text.
System Prompt
Set of instructions given to the model before the user conversation begins that defines its role, behavior, and guidelines for how it should respond. In consumer AI products, the provider writes the system prompt; if you're building an application with an API, you write it yourself.
Token
Units of text a model processes, used to determine cost to use. One token is about ¾ of a word. Input tokens (text given to model) are lower cost than output tokens (text generated by model).
Training
Process for an AI model to learn from data to develop its capabilities. Billions of times, the model is educated by take in data to make a prediction then using feedback to make better predictions. People fine-tune the model with curated data and use Reinforcement Learning from Human Feedback (RLHF) to produce preferred responses.
*AI was used for research