AI Terminology for Dummies
I’ve been working with Artificial Intelligence (AI) for a while, I’ve noticed some key terms that cause some confusion for newcomers (I know I did with some). I thought it would be helpful to have a quick and hopefully simple guide for some of them.
Artificial Intelligence (AI): The field of computer science focused on creating systems that can perform tasks that typically require human intelligence, such as problem-solving, reasoning, and learning.
Machine Learning (ML): A subset of AI where computers learn patterns from data without explicit programming.
Gen AI: Short for Generative AI. This has become a very key use case over the last few years with popularity of tools like GitHub Copilot. It reminds me of the old tool that generate conference submission abstracts (though that was more random tech jargon generator).
Dad joke warning: Alternate definition — Gen-AI is the generation after Gen-Z, if you were born after creation of tools like GitHub Copilot
AI agents: Ever get a popup at the bottom right of your page asking to help? That is basically an AI agent. A common pattern for using both canned and typical topics while combining with direct text inputs, leveraging AI to improve the customer experience.
Agentic AI: This is basically the same as AI agents but a focus more of agents that act autonomously.
Predictive AI: The name pretty much says it all. Leveraging AI to help predict outcomes — like whether users find this post useful, or when my care might need a repair or even replaced, etc.
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ChatGPT: is an advanced AI-powered chatbot developed by OpenAI, based on the GPT (Generative Pre-trained Transformer) architecture. It is designed to generate human-like text responses in a conversational format.
Generated from ChatGPT with prompt “What is ChatGPT?” (shortened).
LLM: Large Language Models are deep learning models around natural language processing (NLP) trained on massive text datasets for tasks like text generation and understanding. ChatGPT is based on GPT LLM.
AIOps: Leveraging AI to assist with all aspects of SRE and IT operations, including risk assessment, remediation, root cause analysis (RCA), to name a few.
MLOps: This could also be called Model Development Lifecycle (MDLC), analogous to Software Development Lifecycle for software applications. It includes everything you need for an individual to developer a model: versioning, development, training, deployments, validation, etc.
MCP: Model Context Protocol (MCP) open protocol that standardizes how applications provide context to LLMs. Simply put, a way to plug in various data sources without any changes to the LLM and provide needed context programmatic.
RAG also called RAG Pipelines: Retrieval Augmented Generation (RAG) Pipelines is a process to fetch data (the right data and fresh/current) and provide any improvements to it to help for the specific use.
I’d love to hear if it helps and if there are any other terms you’d suggest.
If you are looking for a more complete set, check out https://a16z.com/ai-glossary/ (Thanks Jonathan G for the link)
Sorry, I’m not trying to call anyone a Dummy…just a good analogy to the “for Dummies” series that provides a nice summary for wider audiences.
Update 7-Apr-2025: Seeing the terms Agentic AI, MCP and RAG Pipelines a LOT, so I added them.
