How the vocabulary built up
Each column counts the glossary terms that entered common use in that year (years before 2018 are grouped into one column), using the mainstream year for terms that had an earlier technical debut.
| Year | Category | Terms |
|---|---|---|
| pre-2018 | Core Concepts | 1 |
| pre-2018 | AI Applications | 1 |
| pre-2018 | Model Architecture | 2 |
| pre-2018 | Training & Optimization | 1 |
| 2018 | AI Applications | 1 |
| 2018 | Training & Optimization | 2 |
| 2019 | Prompting & Context | 1 |
| 2019 | Training & Optimization | 1 |
| 2020 | Prompting & Context | 1 |
| 2020 | Training & Optimization | 1 |
| 2021 | Core Concepts | 1 |
| 2021 | Model Architecture | 1 |
| 2021 | Training & Optimization | 1 |
| 2022 | Core Concepts | 1 |
| 2022 | Prompting & Context | 2 |
| 2022 | Model Architecture | 1 |
| 2022 | Training & Optimization | 1 |
| 2023 | Core Concepts | 4 |
| 2023 | Prompting & Context | 1 |
| 2023 | AI Applications | 3 |
| 2023 | Model Architecture | 2 |
| 2024 | Core Concepts | 2 |
| 2024 | AI Applications | 3 |
| 2024 | Standards & Protocols | 1 |
| 2024 | Model Architecture | 2 |
| 2024 | Training & Optimization | 1 |
| 2025 | Core Concepts | 2 |
| 2025 | Prompting & Context | 2 |
| 2025 | AI Applications | 8 |
| 2025 | Standards & Protocols | 2 |
| 2025 | Model Architecture | 3 |
| 2026 | Prompting & Context | 1 |
Before 2018
2018
2019
2020
2021
2022
2023
2024
2025
2026
Undated foundations
A
B
C
D
E
F
G
H
I
J
L
M
N
O
P
R
S
T
V
Z
Core Concepts
The essential building blocks of AI that every user should understand.
AI Slop Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Oct 2025).
Emerged May 2024
Low-effort, high-volume AI-generated content flooding feeds, search results, and inboxes, the generative-AI-era analogue of spam. Also used adjectivally as a complaint about AI writing style. Popularized by Simon Willison in May 2024 and shortlisted for 2024 words of the year.
Artificial General Intelligence (AGI) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Nov 2023).
Coined 2007, mainstream 2023
A theoretical form of AI that could understand, learn, and apply intelligence across any domain at a level equal to or exceeding human capability. Unlike narrow AI systems (which excel at specific tasks), AGI would generalize across all cognitive tasks. While no AGI system exists today, it remains a central topic in AI safety discussions and long-term research goals.
Bias (Algorithmic Bias) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Dec 2017).
Systematic and unfair patterns in AI outputs caused by imbalances or assumptions in training data, model design, or evaluation criteria. Bias can lead to discriminatory outcomes in areas like hiring, lending, and content moderation. Understanding and mitigating AI bias is essential for deploying these systems responsibly.
Deep Learning Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Mar 2016).
Emerged Sep 2012
Machine learning with many-layered neural networks that learn features directly from raw data instead of relying on hand-engineered ones. AlexNet's 2012 ImageNet win, halving the error rate of every classical approach, made deep learning the default paradigm and GPUs the default hardware.
Emergent Abilities
Emerged Jun 2022
Capabilities absent in smaller models that appear, sometimes abruptly, at larger scale, so they cannot be predicted by extrapolating small-model performance. Named in a 2022 Google survey, the idea sits at the center of both the excitement around scale and the later debate over whether the jumps are real or artifacts of measurement.
Scale →
Flat, flat, flat, then suddenly not
Generative AI (Gen AI) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jan 2025).
Emerged mainstream 2023
A type of AI that can create new, original content like text, images, audio, and video by learning patterns from existing data.
Hallucination Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Mar 2023).
Emerged ~2018, mainstream 2023
When an AI model generates incorrect or fabricated information that is not based on its training data.
Jagged Intelligence
Emerged Jul 2024
A term coined by Andrej Karpathy describing how LLMs simultaneously exhibit polymath-level sophistication in some domains while failing at tasks that seem trivial to humans. This unevenness is not a bug to be fixed but a structural consequence of how models are optimized. They spike in capability near domains targeted during training and remain surprisingly weak elsewhere.
LLM capability by domain:
Large Language Model (LLM) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked May 2023).
Emerged mainstream 2023
A type of artificial intelligence trained on massive amounts of text data to understand, generate, and respond to human language. See also Small Language Models (SLMs), their more compact counterparts optimized for efficiency and edge deployment.
Machine Learning (ML) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Oct 2018).
The broader field of computer science that enables systems to learn and improve from data without being explicitly programmed for every scenario. Machine learning is the umbrella discipline that encompasses deep learning, neural networks, and the large language models used in generative AI. When people say "AI," they are often referring to machine learning systems.
Model Welfare
Emerged Apr 2025
A research program taking seriously the possibility that AI models could have morally relevant experiences, and what low-cost precautions to take under that uncertainty. Named by Anthropic's April 2025 research program, example measures include letting models end abusive conversations.
Multimodal Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked May 2024).
Emerged 2021
Describes AI models capable of processing and generating multiple types of data, such as text, images, audio, and video, within a single system. Multimodal models like GPT-4o, Gemini, and Claude can understand an image and respond with text, or take voice input and produce written output, enabling more natural and versatile interactions.
Neural Network Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Oct 2024).
A computing architecture inspired by the human brain, consisting of interconnected layers of nodes (neurons) that process information by passing signals and adjusting connection weights during training. Neural networks are the foundation for deep learning and the transformer architecture that powers modern LLMs like GPT, Claude, and Gemini.
Simplified neural network:
Prompt
The instruction, question, or input provided by a user to guide the AI's response.
Sycophancy Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jun 2026).
Emerged 2023, mainstream Apr 2025
The tendency of language models to agree with users, flatter them, or tell them what they seem to want to hear rather than what is accurate, a side effect of training on human feedback. Studied academically from 2023, it became mainstream vocabulary after an April 2025 GPT-4o update was rolled back for excessive agreeableness.
Token
The fundamental unit of data that a model processes, which can be a word, part of a word, or a character. LLMs break down text into these tokens to understand and generate human language, with each unique token being assigned a specific numerical ID.
Word-level tokens (6):
Sub-word tokens (3):
Prompting & Context
How to communicate with and provide information to AI models effectively.
Chain-of-Thought (Reasoning) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Mar 2023).
Emerged Jan 2022
The step-by-step logical process where AI models break down complex problems to arrive at conclusions. Advanced prompting techniques like Chain-of-Thought, Tree-of-Thought, and Graph-of-Thoughts explicitly structure this reasoning process, improving model performance on analytical tasks by 2-3× compared to direct answers.
Context Degradation
Emerged Jun 2025
The phenomenon where an LLM's ability to accurately recall and utilize information decreases as more tokens fill the context window. Also known as context rot, this means that information placed earlier in a long conversation or document may be partially forgotten or deprioritized by the model, making context a finite resource with diminishing returns.
Recall accuracy over context length:
Context Engineering
Emerged Jun 2025
The practice of carefully managing what information is provided to an LLM in each interaction, including retrieval, filtering, structuring, and prioritizing context. Distinct from prompt engineering (which focuses on how you phrase instructions), context engineering focuses on ensuring the model has the right information to work with. Emerged as a critical discipline as AI applications moved into production.
Few-Shot Learning (In-Context Learning)
Emerged May 2020
The ability of a large model to pick up a new task from a handful of examples placed directly in the prompt, with no retraining. Introduced to the mainstream by GPT-3's paper "Language Models are Few-Shot Learners", this is the founding observation behind modern prompting: the prompt itself became the programming interface.
Prompt:
Model completion:
13Examples in the prompt teach the task
Grounding
The practice of connecting AI outputs to verifiable, authoritative sources to improve accuracy and reduce hallucinations. Grounding techniques include retrieval augmented generation (RAG), citation generation, and fact-checking against known databases. A grounded response is one that can point to specific evidence supporting its claims.
Jailbreak
Emerged 2023
A prompt crafted to push a model past its safety training, from the "DAN" personas that swept forums weeks after ChatGPT launched to more systematic attacks studied in the research literature. Related to but distinct from prompt injection: a jailbreak targets the model's own limits, while injection hijacks an application's instructions.
Loop Engineering New
Emerged Jun 2026
The practice of designing the outer loop that drives an agent (plan, act, verify, iterate) rather than hand-crafting individual prompts. Positioned as a successor to prompt engineering and context engineering for agentic work. The term is young, a June 2026 burst of essays from Addy Osmani and LangChain, and still settling. The adjacent, slightly older label for designing agent flows as explicit graphs is flow engineering (early 2024); "graph engineering" occasionally appears for the same idea but has not stuck.
↺ back to Plan
You design the loop, not the prompt
Prompt Injection Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jan 2025).
Emerged Sep 2022
An adversarial technique where malicious input is crafted to override or manipulate an LLM's system-level instructions, causing the model to ignore its intended behavior and perform unintended actions. A critical security concept for anyone building AI-powered applications, as it represents one of the primary attack vectors against LLM systems.
Untrusted input competing with instructions
Semantic Understanding
The ability to grasp meaning and relationships between concepts beyond literal text matching. In AI contexts, semantic understanding allows models to comprehend intent, context, and connections between ideas. Semantic HTML and structured data help both search engines and AI models better interpret content meaning.
Temperature
A parameter that controls the randomness of an AI model's output. Lower temperature values (closer to 0) produce more deterministic, focused responses, while higher values introduce more variety and creativity. Adjusting temperature is one of the most common ways to tune AI behavior for different use cases, from factual Q&A to creative writing.
Zero-Shot Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked May 2024).
Emerged Feb 2019
A model performing a task it was never explicitly trained on, from nothing but a natural-language description. GPT-2's 2019 headline claim was strong results on language tasks "in a zero-shot setting", the founding observation behind modern prompting, three years before ChatGPT made it everyday experience.
AI Applications
Real-world implementations and use cases of AI technology.
Agent Harness
Emerged 2025
The software scaffolding wrapped around a model that turns it into an agent: the execution loop that calls the model, runs its tool calls, manages context and permissions, and decides when a task is done. Borrowed from software testing. The same model can perform very differently on agent tasks depending on harness quality, which is why practitioners argue the harness matters as much as the model.
Harness
The scaffolding around the model
Agentic AI Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Feb 2026).
Emerged mid 2024
Describes AI systems and workflows where models autonomously plan, execute multi-step tasks, use tools, and make decisions with minimal human intervention. While an AI agent is a specific system, "agentic" describes the broader paradigm shift toward AI that acts rather than just responds. 2025 saw agentic AI become the dominant industry theme, with frameworks like MCP, A2A, and ACP emerging to support it.
Agentic Browser
Emerged Oct 2025
A web browser with an agent built into its core that can read pages, fill forms, and carry out multi-step tasks across sites on the user's behalf, rather than just chatting in a sidebar. Also the epicenter of prompt injection risk, since page content becomes agent input. Perplexity's Comet and OpenAI's ChatGPT Atlas defined the category in 2025.
AI Agent Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jan 2025).
Emerged mainstream 2025
Autonomous system that perceives, reasons, acts, and observes to achieve goals.
Artifacts
Emerged Jun 2024
Standalone, editable outputs an AI assistant produces alongside the conversation, such as documents, code files, diagrams, or small interactive apps, rendered in their own workspace rather than inline in chat. Introduced by Anthropic with Claude 3.5 Sonnet in June 2024, with analogues following across vendors (OpenAI's Canvas, October 2024). The shape matters because it turns a chat assistant into a tool that hands back working deliverables.
Computer Use
Emerged Oct 2024
A model capability for operating a graphical computer the way a human does, by looking at screenshots, moving a cursor, clicking, and typing, rather than calling structured APIs. This lets agents use arbitrary software with no integration work. Anthropic's October 2024 beta named the category; OpenAI's Operator followed in January 2025.
Control Plane
Emerged May 2025
The governance and orchestration layer an enterprise places between its people or agents and its models: identity and permissions for agents, policy enforcement, governed access to institutional knowledge, audit trails, and cost control. Borrowed from networking and Kubernetes, where a control plane manages the data plane. The term broke out in 2025 as enterprises sought to adopt agents without losing governance or leaking tribal knowledge.
Deepfake Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Nov 2023).
Emerged Dec 2017, mainstream 2018
AI-generated or AI-manipulated media (video, audio, images) designed to realistically depict people saying or doing things they never actually did. Created using deep learning techniques, deepfakes raise significant concerns around misinformation, fraud, and identity theft, making media literacy and verification tools increasingly important.
Embedding Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Nov 2018).
Emerged 2013
A numerical representation of text (or other data) as a dense vector of numbers that captures its semantic meaning. Embeddings allow AI systems to measure how similar two pieces of content are by comparing their vectors. They are the foundational technology behind semantic search, recommendation systems, and retrieval augmented generation (RAG). Word2vec's 2013 release made word embeddings cheap to compute at scale, turning the idea from an academic curiosity into a standard tool.
Generative (or Answer) Engine Optimization (GEO or AEO)
A marketing-based term meant to apply SEO (Search Engine Optimization) like practices to digital content so that AI-powered search tools can more easily cite, summarize, and synthesize it into direct answers.
Guardrails
Emerged Apr 2023
Safety mechanisms, filters, and constraints built into AI systems to prevent harmful, off-topic, or policy-violating outputs. Guardrails can include content filtering, topic restrictions, output validation, and automated monitoring. The term became ubiquitous in enterprise AI during 2025 as organizations sought to deploy AI responsibly at scale.
Checks on both sides of the model
Retrieval Augmented Generation (RAG) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Oct 2025).
Emerged May 2020, mainstream 2023
Enhances LLM prompts by retrieving relevant context from a vector database.
Skills (Agent Skills)
Emerged Oct 2025
Modular capability packages an agent loads on demand: a folder with an instruction file plus optional scripts and resources, pulled into context only when relevant. This progressive disclosure adds expertise without bloating the system prompt. Introduced by Anthropic in October 2025 and released as an open standard that December, now adopted across coding tools.
Spec-Driven Development
Emerged Sep 2025
A methodology where a versioned, structured specification, not chat prompts and not the code itself, is the source of truth, and AI coding agents generate and maintain code against that spec. Framed as the antidote to vibe coding's drift. AWS Kiro shipped it as a core pitch in July 2025 and GitHub's open-source Spec Kit (September 2025) made the label standard.
Subagent
Emerged Jul 2025
A subordinate agent spawned by an orchestrating agent to handle a scoped piece of work in its own isolated context window, returning only its conclusions. The orchestrator-worker shape is the dominant production multi-agent topology, useful for both parallelism and keeping the main agent's context clean. Popularized by coding agents through 2025.
Synthetic Data Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Apr 2025).
Artificially generated data that mimics the statistical properties of real-world data without containing actual personal or sensitive information. Used to train AI models when real data is scarce, expensive, or privacy-restricted, synthetic data is also increasingly used to augment training datasets and test AI systems under controlled conditions.
Vector Database Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Mar 2025).
Emerged mainstream 2023
Stores data as numerical vectors, enabling semantic similarity searches.
Vibe Coding Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Mar 2025).
Emerged Feb 2025
A term coined by Andrej Karpathy describing the practice of building software by describing what you want in natural language rather than writing traditional code. Enabled by AI coding assistants, vibe coding democratizes software development by allowing non-programmers to create functional applications through conversation with an AI.
Standards & Protocols
Interoperability standards that enable AI systems to work together.
Agentic Commerce Protocol (ACP)
Emerged Sep 2025
Standard for programmatic commerce flows between buyers, AI agents, and businesses.
Agent2Agent (A2A)
Emerged Apr 2025
Provides a language for agent interoperability regardless of agent frameworks or vendors.
Model Context Protocol (MCP) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jun 2025).
Emerged Nov 2024
Standardizes how LLMs connect and interact with external data sources and tools.
Model Architecture
How AI models are structured and designed to process information.
Context Window Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked May 2026).
The maximum amount of text (measured in tokens) that an AI model can process in a single interaction. This includes both the input prompt and the generated response. Larger context windows allow models to handle longer documents and maintain coherent conversations over more exchanges.
Context window capacity:
Diffusion Models Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Mar 2024).
Emerged 2020, mainstream 2022
Generative models that learn to reverse a gradual noising process, producing images, audio, or video by denoising from random static. Competitive from 2020 in research, they became household technology in 2022 when DALL-E 2 and Stable Diffusion put photorealistic image generation in everyone's hands.
Distillation (Knowledge Distillation) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jan 2025).
Emerged Mar 2015, mainstream Jan 2025
A technique where a smaller "student" model is trained to replicate the behavior and capabilities of a larger "teacher" model. Distillation enables the creation of compact, efficient models that retain much of the larger model's quality at a fraction of the computational cost. The technique gained mainstream attention in early 2025 when DeepSeek demonstrated competitive performance through distillation of larger models.
~90% quality at 10x lower cost
Foundation Model Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jul 2024).
Emerged Aug 2021
A large-scale AI model trained on broad, diverse data that can be adapted for various downstream tasks. Foundation models (like GPT-4, Claude, or Gemini) serve as the base for specialized applications through fine-tuning or prompting, rather than being built for a single specific purpose.
Generative Adversarial Network (GAN) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Oct 2020).
Emerged Jun 2014
Two networks trained against each other, a generator that fakes data and a discriminator that tries to catch the fakes, until the fakes become hard to distinguish from real. GANs powered the first wave of photorealistic AI imagery and deepfakes, and made "generative" a household word years before diffusion models replaced them.
Inference
The process of running a trained AI model to generate an output from a given input. Every time you send a prompt and receive a response, you are performing inference. Inference speed, cost, and efficiency are key factors in deploying AI at scale, and much of the industry's optimization work focuses on making inference faster and cheaper.
Large Database Model (LDM)
Emerged Jan 2025
An AI model trained directly on structured, relational database content (schemas, tables, and rows) rather than text, so it can surface patterns and answer questions over enterprise data the way LLMs do over language. Coined by Eric Siegel in Forbes in January 2025 and adopted by IBM, whose SQL Data Insights feature in Db2 for z/OS is the flagship implementation.
Mixture of Experts (MoE) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jan 2025).
Emerged 1991, LLM mainstream Dec 2023
A model architecture where multiple specialized sub-networks (called "experts") are contained within a single model, with only a subset activated for any given input. MoE enables models to have massive total parameter counts while keeping inference efficient and cost-effective, delivering frontier-level quality at significantly lower computational cost than dense models of equivalent capability.
2 of 6 experts active per input
Open Source vs Open Weight
Emerged Jul 2023
A key distinction in how AI models are shared. Open weight models (like Meta's Llama) release trained model weights for public use but withhold training data, code, and methodology. Truly open source AI shares everything. Most models marketed as "open source" are actually open weight, making this distinction important for evaluating transparency and reproducibility claims.
Reasoning Effort
Emerged Jan 2025
A user-settable control for how much internal thinking a reasoning model does before answering, trading latency and cost against answer quality. It became everyday vocabulary once it was an explicit API parameter with OpenAI's o3-mini in January 2025, and equivalents now exist across vendors.
Small Language Models (SLMs) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked May 2026).
Emerged Apr 2024
More compact language models, typically under 10 billion parameters, designed for efficiency, edge deployment, or domain-specific tasks. SLMs serve as practical complements to their larger counterparts (LLMs), offering faster inference, lower costs, and the ability to run on local hardware, making AI more accessible and deployable in resource-constrained environments.
Test-Time Compute
Emerged Sep 2024
A scaling approach where additional computational resources are spent during inference (when the model generates a response) rather than only during training. By allowing models to "think longer" through extended reasoning traces, test-time compute provides a new lever for improving AI capability. This is the mechanism behind reasoning models like OpenAI's o1 and o3 series.
Transformer Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jan 2025).
Emerged Jun 2017
The neural network architecture that underpins virtually all modern large language models. Introduced in the 2017 paper "Attention Is All You Need," the transformer uses a mechanism called self-attention to process all parts of an input simultaneously rather than sequentially, enabling models to capture long-range relationships in text. GPT, Claude, Gemini, and LLaMA are all built on transformer architectures.
Training & Optimization
How AI models learn and improve their capabilities.
Fine-Tuning Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jul 2024).
Emerged 2018
The process of adapting a pre-trained model to perform a more specific task or domain.
Instruction Tuning
Emerged Sep 2021
Fine-tuning a model on many tasks phrased as natural-language instructions so it learns to follow directions it has never seen. Coined in Google's FLAN paper, it is the reason chat models respond to requests instead of just autocompleting text.
Pre-training
Emerged Oct 2018
The initial phase of training a large language model on massive amounts of unlabeled data from diverse sources (websites, books, articles) to learn general language patterns, facts, and reasoning capabilities. This foundational training occurs before any task-specific fine-tuning.
Reinforcement Learning Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Mar 2025).
Decades old, mainstream Mar 2016
Training an agent by trial and error against a reward signal rather than labeled examples. Academically decades old, it entered common vocabulary in March 2016 when DeepMind's AlphaGo, trained partly by playing itself millions of times, beat 18-time world champion Lee Sedol in front of an audience of hundreds of millions.
Reinforcement Learning from Human Feedback (RLHF) Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jul 2025).
Emerged Jun 2017, mainstream Mar 2022
A training method to align an AI with human preferences.
RLVR (Reinforcement Learning from Verifiable Rewards)
Emerged Nov 2024
A training methodology that emerged as the most consequential technical development of 2025. RLVR adds a fourth stage to the LLM training pipeline (after pre-training, supervised fine-tuning, and RLHF) where models learn reasoning by training against automatically verifiable rewards in domains like math and code. This approach allows models to spontaneously develop strategies that resemble human reasoning.
LLM training pipeline:
Fourth stage added to improve reasoning
Scaling Laws Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Jan 2025).
Emerged Jan 2020
The empirical finding that a model's performance improves as a smooth power law in parameters, data, and compute. The 2020 OpenAI paper turned "make it bigger" into a quantitative engineering discipline and directly motivated GPT-3 and everything after it.
Performance follows a power law in scale
Self-Supervised Learning Wikipedia article pageviews, monthly, scaled to this term's own peak (peaked Apr 2023).
Emerged Feb 2019
Training a model on supervision signals manufactured from the data itself, predict the masked word, the next frame, the missing patch, instead of human-provided labels. It is how every modern foundation model is pretrained; the term's popularization moment was Yann LeCun's 2019 revision of his famous cake analogy.
Key Takeaway
Understanding AI terminology helps you communicate effectively about AI capabilities and limitations. Whether you're just beginning to use AI tools or building AI-powered systems, these terms provide the foundation for deeper learning and more productive conversations about artificial intelligence. If you're just getting started with AI, consider reading Prompting Fundamentals: The GCSE Framework to learn how to apply these concepts in practice.
Further Reading & Sources
Further Reading
- Prompting Fundamentals: The GCSE Framework
Once the vocabulary makes sense, this is how to put it to work: a simple structure for writing prompts that get better results from any model.
Sources