PM's AI Glossary: 25+ Key Terms Made Simple
Lead technical conversations with confidence.
AI is no longer a niche engineering topic; it's a core component of product strategy. As product managers, we don't need to build the models ourselves, but we must speak the language to identify opportunities, define requirements, and lead our teams effectively.
This post is your reference guide. Your Glossary. It's a no-nonsense glossary of over 25 essential AI terms, translated into plain English with examples you can actually use. Let's demystify the jargon.
Part 1: The Foundational Concepts
These are the big-picture terms you will hear most often.
1. Artificial Intelligence (AI)
Simple Explanation: The broad science of making computers perform tasks that typically require human intelligence, like understanding language, recognizing images, or making decisions.
PM Example: When you ask Siri or Google Assistant to set a timer, that's AI. It understands your request and takes action.
2. Machine Learning (ML)
Simple Explanation: A subset of AI where systems learn and improve from data without being explicitly programmed. Instead of writing rules, you feed it examples.
PM Example: Your Netflix homepage is a product of ML. The system learns from your viewing history to recommend shows you're likely to watch.
3. Deep Learning
Simple Explanation: A more advanced subset of ML that uses complex, multi-layered "neural networks" to solve very complex problems, often with massive amounts of data.
PM Example: The "Face ID" feature on your phone uses deep learning to recognize the unique patterns of your face, even if you're wearing glasses or growing a beard.
4. Generative AI
Simple Explanation: A category of AI that doesn't just analyze or classify data, but creates new content, like text, images, music, or code.
PM Example: Using ChatGPT to draft an email or Midjourney to create a unique image for a presentation are classic examples of generative AI.
5. Large Language Model (LLM)
Simple Explanation: The engine behind most modern text-based Generative AI. It's a massive model trained on a huge amount of text from the internet so it can understand and generate human-like language.
PM Example: The core technology behind ChatGPT, Google's Gemini, and Anthropic's Claude is an LLM.
Part 2: How Models Learn and Work
The vocabulary for the "learning" part of Machine Learning.
6. Algorithm
Simple Explanation: The set of rules or mathematical process a computer follows to solve a problem or make a prediction.
PM Example: In a food delivery app, an algorithm calculates the "Estimated Time of Arrival (ETA)" based on factors like restaurant prep time, driver location, and traffic.
7. Model
Simple Explanation: The output of a machine learning algorithm after it has been "trained" on data. The model is the file or system that makes the actual predictions.
PM Example: After training an algorithm on thousands of customer support tickets, the resulting "model" can now predict whether a new, incoming ticket is "Urgent" or "Not Urgent."
8. Training Data
Simple Explanation: The dataset used to teach a machine learning model. The quality and quantity of this data are crucial for the model's accuracy.
PM Example: To build a spam filter, you need training data consisting of thousands of emails, each one labeled as either "Spam" or "Not Spam."
9. Supervised Learning
Simple Explanation: A type of ML where the model learns from labeled data (like the spam example above). You "supervise" the learning by providing the correct answers.
PM Example: Building a feature that identifies pictures of cats. You train the model with a dataset where every image is clearly labeled "cat" or "not a cat."
10. Unsupervised Learning
Simple Explanation: A type of ML where the model finds hidden patterns in data that has not been labeled.
PM Example: A customer segmentation feature that automatically groups your users into personas based on their behavior, without you defining the groups beforehand.
11. Reinforcement Learning
Simple Explanation: A type of ML where a model learns by trial and error, receiving "rewards" for good actions and "penalties" for bad ones.
PM Example: An AI opponent in a chess game learns to win by being rewarded for moves that lead to capturing pieces and penalized for moves that lead to being checkmated.
12. Neural Network
Simple Explanation: The computing systems, inspired by the human brain, that power Deep Learning. They are made of interconnected nodes (or "neurons") in layers that process information.
PM Example: The technology behind Google Translate uses a complex neural network to understand the grammar and context of a sentence before translating it.
13. Parameters
Simple Explanation: The internal variables or "settings" that a model learns from the training data. Think of them as the knobs the model turns to tune its predictions.
PM Example: In a model that predicts house prices, parameters might be the weights it assigns to "number of bedrooms" and "square footage."
Part 3: The Jargon of Generative AI
These are the terms flying around in every conversation about LLMs today.
14. Prompt
Simple Explanation: The instruction or question you give to a generative AI model to get a response.
PM Example: When you type "Write a user story for a login page" into ChatGPT, that text is your prompt.
15. Hallucination
Simple Explanation: When an AI model confidently makes up false or nonsensical information. It's essentially guessing and presenting it as fact.
PM Example: Asking an AI chatbot for a legal precedent and it invents a court case that never happened. This is a critical risk to manage in product development.
16. Fine-Tuning
Simple Explanation: Taking a pre-trained general model (like a base LLM) and training it a little more on your own specific, high-quality data to make it an expert for your use case.
PM Example: A hospital could fine-tune a general LLM on its internal medical documentation to create a chatbot that understands its specific terminology and procedures.
17. Retrieval-Augmented Generation (RAG)
Simple Explanation: A popular method to make LLMs more accurate and up-to-date. Instead of just relying on its training data, the model first "retrieves" relevant, current information from a specific knowledge base (like your company's Confluence pages) and then uses that info to "augment" its answer.
PM Example: You build a customer support bot using RAG. When a user asks about the return policy for a product bought last week, the bot retrieves the current return policy from your website before generating the answer, ensuring it's accurate.
18. Embeddings
Simple Explanation: A way of converting words, sentences, or even images into a list of numbers (a "vector"). This allows models to understand the relationships and semantic meaning between different pieces of content.
PM Example: A search feature that uses embeddings can understand that a query for "what to wear in cold weather" is semantically similar to documents containing the words "jackets," "sweaters," and "winter coats," even if the exact search words aren't there.
19. Vector Database
Simple Explanation: A special type of database designed to efficiently store and search through embeddings (those lists of numbers).
PM Example: RAG systems almost always use a vector database to quickly find the most relevant documents to feed to the LLM.
20. Transformer
Simple Explanation: The groundbreaking architecture or "design" behind most modern LLMs (the 'T' in ChatGPT stands for Transformer). Its key innovation is the ability to weigh the importance of different words in a sentence, which gives it a much better understanding of context.
PM Example: You don't need to build one, but you should know that this is the foundational technology that made today's powerful AI chatbots possible.
21. Token
Simple Explanation: The small pieces of text—like words or parts of words—that an LLM processes. "I am a PM" might be broken into 5 tokens: "I", "am", "a", "P", "M".
PM Example: Most LLM API pricing is based on the number of tokens in your prompt and the model's response. Understanding this is key to managing the cost of your AI feature.
22. Context Window
Simple Explanation: The amount of text (measured in tokens) that a model can "remember" and consider at one time. This includes both your prompt and its response.
PM Example: A model with a small context window might "forget" what you talked about at the beginning of a long conversation. A larger context window allows for more coherent, multi-turn dialogues.
23. Inference
Simple Explanation: The process of using a trained model to make a prediction or generate a response. It's the "live" phase after the model has been trained.
PM Example: When a user types a message and your AI feature generates a reply, the act of generating that reply is called inference.
Part 4: Common Applications & Metrics
24. Natural Language Processing (NLP) / Understanding (NLU)
Simple Explanation: The field of AI focused on enabling computers to understand, interpret, and generate human language. NLU is the "understanding" part.
PM Example: Sentiment analysis on customer reviews (classifying them as positive, negative, or neutral) is a classic NLP/NLU product feature.
25. Computer Vision
Simple Explanation: The field of AI focused on enabling computers to "see" and interpret information from images and videos.
PM Example: The feature in your photo app that automatically groups pictures of the same person uses computer vision.
26. Bias
Simple Explanation: When an AI model produces prejudiced or unfair results because it was trained on biased data that reflects existing human biases.
PM Example: A resume-screening AI that was trained on historical hiring data might learn to unfairly favor male candidates. As a PM, identifying and mitigating bias is a critical ethical responsibility.
With these lexicons, you can unlock more productive conversations with your Data Scientists and Engineers, ask more insightful questions, and move from being a participant in the AI revolution to a thoughtful leader.
Your true value as a Product Manager is to be the bridge between what is technically possible and what is genuinely valuable for your customers.
Understanding this language allows you to stand firmly on that bridge, translating complex concepts into compelling product vision.
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