AI Product Management Glossary: 80+ Key Terms Every PM Should Know

October 11, 2025
product management
4 min read

By Srishti Sharma– Product Marketer

AI is changing how we build products every single day. From chatbots that reply like real people to smart systems that recommend what you should watch or buy, AI is now a part of almost everything we use.

But with AI comes a lot of new words and ideas that can feel confusing at first – LLMs, RAG, RLHF, MoE, and many more. If you’re a Product Manager, AI engineer, or just someone working with AI tools, you need to know what these words mean to do your job well.

This glossary pulls together 80+ simple, clear terms across 12 categories. It’s here to help you talk to your team, understand what’s going on in AI projects, and make better decisions about what to build next.

Key Takeaways:

AI Product Management blends technical AI knowledge with customer value and business viability focus.
Generative AI, LLMs, and models like MoE, VLMs, and SAMs are core to building modern AI products.
Prompting and context engineering are crucial for getting accurate, reliable model outputs.
RAG, AI agents, and feature stores help connect models with real-world data for better performance.
Safety, ethics, and continuous evaluation ensure AI products are trustworthy, fair, and effective over time.

In this article

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AI Product Management

AI Literacy & Intuition: Being AI-literate means knowing the basics; large language models (LLMs), AI agents, model evaluation; and developing an instinct for how AI behaves. The best way to build this intuition is through hands-on experimentation, not just reading theory.

AI Product Manager: A Product Manager who works specifically on AI products and features. This role requires technical understanding and a strong AI intuition to guide product decisions.

AI-Powered Product Manager: A PM who uses AI tools every day to be more productive, from automating tasks to improving decision-making. In the near future, this will be the norm.

Product Manager’s Core Risks: Every PM is responsible for two big questions:

Customer Value: Will customers want and use this product?
Business Viability: Can the business deliver this product sustainably?

The best PMs spend time in Product Discovery to answer these questions before building.

Generative AI: Basics

Deep Learning: Neural networks with many layers that can learn complex patterns.
Fine-Tuning: Taking a pre-trained model and teaching it to handle a specific use case.
Generative AI: AI that creates new text, images, audio, or code by learning from data.
Neural Networks: Systems inspired by the brain, made of “neurons” that pass signals.
Reinforcement Learning: The model learns by trial and error, receiving rewards for good decisions.
RLHF: Reinforcement learning from human feedback, fine-tuning models to match human preferences.
Supervised & Unsupervised Learning: Models that learn with labeled data (supervised) or by finding patterns on their own (unsupervised).

Transformers: The backbone of LLMs, using self-attention to understand context.

AI Models

Large Language Models (LLMs): Models trained on huge datasets to understand and generate text.
Small Language Models (SLMs): Lighter, faster models for specialized use cases.
Vision-Language Models (VLMs): Models that can process both text and images.
Mixture of Experts (MoE): Combines multiple models and routes queries to the right “expert.”
Segment Anything Models (SAM): Highly accurate image segmentation models.

Prompting & Context Engineering

Prompting is an art. The way you ask matters.

Chain-of-Thought Prompting: Ask the model to think step-by-step for better reasoning.
Context Engineering: Carefully choose what context to provide so the model stays focused.
Few-Shot & Zero-Shot Learning: Show the model a few examples (few-shot) or none (zero-shot) and let it figure out the task.
Prompt Injection: A security risk where prompts trick the model into bypassing rules.
Temperature: Controls how creative or predictable the output is.

Retrieval-Augmented Generation (RAG)

RAG combines external knowledge with AI responses.

Embedding: Turning text/images into numbers that represent meaning.
Hybrid RAG: Combines keyword and semantic search for better results.
Reranking: Reorders results so the most relevant ones come first.
Vector Databases: Store embeddings for fast, scalable search.
HyDE: Uses hypothetical answers to improve retrieval accuracy.

AI Agents

Agents are like “autonomous co-workers” powered by AI.

AI Agent: A model that chooses what steps to take, not just what answer to give.
Prompt Chaining: Breaking a complex task into smaller steps handled in sequence.
Multi-Agent Systems: Multiple agents collaborating to solve bigger problems.
MCP (Model Context Protocol): A universal standard for connecting AI models to tools and data.

Data & Feature Management

Data Drift & Concept Drift: When real-world data changes, model accuracy drops.
Data Annotation: Labeling data to train models.
Data Augmentation: Expanding datasets with small variations to improve generalization.
Feature Store: A shared “library” of clean, reusable features for ML models.

AI Evals (Evaluation Systems)

AI products need measurement just like any other product.

Error Analysis: Review model outputs to find failure patterns.
LLM Traces: Step-by-step record of a model’s reasoning and output.
F1-Score, Precision, Recall: Metrics to measure performance.
LLM-as-Judge: Using one model to evaluate the output of another.

Deployment & Operations

Latency: Time it takes for the model to respond.
MLOps/ModelOps: Managing models from training to production and monitoring.
Model-as-a-Service (MaaS): Use cloud APIs instead of building models from scratch.
Throughput: How many requests the system can handle per second.

Model Performance & Cost

Inference: The process of generating answers from a trained model.
TTFT (Time to First Token): How quickly the first word shows up.
Tokens Per Second: Speed of text generation.
Quantization: Compressing models to make them faster and cheaper to run.

Safety, Ethics & Alignment

Bias & Fairness: Reducing harmful patterns in model outputs.
Explainability: Helping humans understand how AI made a decision.
Hallucination: When the AI “makes stuff up.”
Human-in-the-Loop: Keeping humans in control for sensitive use cases.

Neural Networks & Transformers

Loss Function: Measures how wrong the model’s predictions are.
Backpropagation: The process that helps models learn from mistakes.
Positional Encoding: Adds order information to help models understand sequences.
Self-Attention: Allows the model to focus on the most important words in context.

AI is a fast-moving field, and this glossary is a living resource to keep you aligned with the latest thinking. Whether you’re building an AI product or just starting your journey, knowing these terms will make you a more confident and effective product manager.