Artificial intelligence is quickly becoming part of day-to-day life for many people: The portion of adults in the U.S. who reported ever using ChatGPT nearly doubled between July 2023 and February/March 2025, rising from 18 percent to 34 percent, according to Pew Research Center.
As AI becomes increasingly prevalent in our personal and professional lives, it’s vital for IT professionals and business leaders to ensure they understand the terminology surrounding AI solutions. Becoming well-versed in this technology and its associated vocabulary is essential if you want to stay current and competitive in the age of omnipresent AI.
If you want to get up to speed on all the must-know artificial intelligence concepts, this AI guide is for you. Note: This isn’t an exhaustive list of AI terminology, but it provides a solid foundation for understanding the technology and what it can do for you.
1. Artificial intelligence
Artificial intelligence simulates human intelligence with machines or computers (Source: Coursera).
2. Machine learning (ML)
Machine learning is the process by which computers use algorithms to analyze data and situations and learn on their own. Machine learning allows programs to become more skilled and accurate with time (Source: edX.org).
3. Large language model (LLM)
A large language model can process and generate sequences of words. LLMs have the ability to participate in conversations, generate written content and analyze text (Source: MIT).
4. Retrieval augmented generation (RAG)
Retrieval augmented generation (RAG) occurs when AI models interact with external sources provided by the end user, such as documents and PDFs. This provides vital context and allows the AI model to provide more thorough and accurate answers to users’ queries (Source: MIT).
5. Natural language processing (NLP)
Natural language processing (NLP) allows AI to understand and produce text and speech. NLP combines statistical, linguistic and AI models (Source: UiPath).
6. Generative AI (genAI)
Generative AI uses LLMs to create new content like images, text, code or videos (Source: Microsoft).
7. Neural network
A neural network is structured like the human brain and can absorb large data sets. Neural networks are a type of deep learning and support functions such as speech recognition (Source: Coursera).
8. Agentic AI
An agentic AI solution can function and make decisions on its own without needing constant human prompting or supervision. Agentic AI systems combine LLMs, NLP and machine learning (Source: edX.org).
9. Hallucinations
Generative AI will fabricate information at times. Developers have labeled these inaccurate responses as hallucinations. This is why AI tools and content require human editing, fact-checking and oversight (Source: Microsoft).
10. AI governance
AI governance frameworks include standards, processes and guardrails to address potential safety and ethics concerns related to AI systems. Without governance, risk and compliance (GRC) features, AI can cause harm through biases and errors (Source: IBM).
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