#104 🤖 AI Terminology Cheatsheet: 100 Essential Terms Every Product Manager Should Master
As AI continues to reshape industries, product managers need to develop fluency in key AI concepts to effectively lead teams and drive innovation.
In today's rapidly evolving tech landscape, AI terminology can feel like learning a new language. But as product managers increasingly work with AI-powered features and capabilities, understanding this vocabulary isn't just nice-to-have—it's essential for success. This comprehensive cheatsheet will equip you with 100 must-know AI terms to help you communicate confidently with technical teams, make informed decisions, and lead the development of AI-enhanced products.
🧠 Foundational AI Concepts
1. Artificial Intelligence (AI): Technology enabling machines to simulate human intelligence and perform tasks that typically require human cognition.
2. Machine Learning (ML): A subset of AI where systems learn from data without explicit programming.
3. Deep Learning (DL): Machine learning using neural networks with multiple layers to analyze various factors of data.
4. Neural Network: Computing systems inspired by the human brain's biological neural networks, consisting of "neurons" that transmit signals.
5. Algorithm: A set of rules or instructions for solving a problem or completing a task.
6. Training: The process of teaching a machine learning model using data.
7. Inference: Using a trained model to make predictions on new data.
8. Supervised Learning: ML technique where the model is trained on labeled data with correct answers.
9. Unsupervised Learning: ML technique where models find patterns in unlabeled data.
10. Reinforcement Learning: ML technique where agents learn optimal behaviors through rewards and penalties.
🔤 Language Models & NLP
11. Natural Language Processing (NLP): AI's ability to understand, interpret, and generate human language.
12. Large Language Model (LLM): AI systems trained on vast amounts of text data to understand and generate human-like text.
13. Transformer: Neural network architecture that powers modern language models like GPT and BERT.
14. GPT (Generative Pre-trained Transformer): A family of language models developed by OpenAI.
15. BERT (Bidirectional Encoder Representations from Transformers): Google's language model that processes words in relation to all other words in a sentence.
16. Tokenization: Breaking text into smaller units called tokens for processing.
17. Embedding: Converting words or phrases into numerical vectors that capture semantic meaning.
18. Fine-tuning: Adapting a pre-trained model to a specific task or domain with additional training.
19. Prompt Engineering: Crafting effective inputs to guide AI systems toward desired outputs.
20. Context Window: The amount of text an LLM can consider at once when generating responses.
🎨 Generative AI
21. Generative AI: AI systems that create new content like text, images, audio, or code.
22. Diffusion Model: Type of generative model that gradually transforms random noise into structured data.
23. GAN (Generative Adversarial Network): System where two neural networks compete to generate realistic outputs.
24. Text-to-Image: Models that generate images from text descriptions (e.g., DALL-E, Midjourney).
25. Text-to-Speech: Converting written text into spoken words.
26. Speech-to-Text: Converting spoken language into written text.
27. Text-to-Video: Converting text descriptions into video content.
28. Synthetic Data: Artificially created data that mimics real-world data.
29. Style Transfer: Applying the artistic style of one image to another.
30. Deepfake: Synthetic media where a person's likeness is replaced with someone else's.
🔍 AI Enhancement & Implementation
31. RAG (Retrieval-Augmented Generation): Enhancing LLM outputs by retrieving relevant information from external sources.
32. Vector Database: Specialized database for storing and querying vector embeddings.
33. Semantic Search: Finding information based on meaning rather than exact keyword matches.
34. Knowledge Graph: Network representing relationships between entities to enhance AI understanding.
35. Transfer Learning: Using knowledge gained from solving one problem to help solve a different but related problem.
36. Few-Shot Learning: AI's ability to learn from a small number of examples.
37. Zero-Shot Learning: AI's ability to perform tasks it wasn't explicitly trained on.
38. Ensemble Learning: Combining multiple models to improve performance.
39. AutoML: Automated machine learning that handles model selection and hyperparameter tuning.
40. MLOps: Practices for deploying and maintaining ML models in production.
🤹 AI Agents & Automation
41. AI Agent: Autonomous system that observes its environment and takes actions to achieve goals.
42. Multi-Agent System: Multiple AI agents working together to solve problems.
43. Autonomous System: System that operates without human intervention.
44. RPA (Robotic Process Automation): Software robots automating repetitive tasks.
45. Chatbot: Conversational AI program simulating human conversation.
46. Virtual Assistant: AI application providing personalized assistance.
47. Agentic Workflow: Series of tasks executed by AI agents to achieve complex goals.
48. Tool Use: AI's ability to use external tools and APIs to accomplish tasks.
49. Planning: AI's ability to create a sequence of actions to achieve a goal.
50. Metaprompting: AI generating prompts for itself to solve complex problems.
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🧮 Technical Implementation
51. API (Application Programming Interface): Connection allowing applications to communicate with AI models.
52. Parameter: Variable that the model learns during training.
53. Hyperparameter: Configuration variable set before training begins.
54. Batch Size: Number of training examples used in one forward/backward pass.
55. Epoch: One complete pass through the entire training dataset.
56. Loss Function: Measures how well the model's predictions match the actual data.
57. Overfitting: When a model performs well on training data but poorly on new data.
58. Underfitting: When a model is too simple to capture the underlying pattern in the data.
59. GPU (Graphics Processing Unit): Hardware accelerator for training AI models.
60. TPU (Tensor Processing Unit): Google's custom-designed AI accelerator chip.
📊 Data & Evaluation
61. Training Data: Data used to train the model.
62. Validation Data: Data used to tune hyperparameters and prevent overfitting.
63. Test Data: Data used to evaluate the model's final performance.
64. Data Augmentation: Techniques to artificially increase the size of a training dataset.
65. Feature: Input variable used in a machine learning model.
66. Feature Engineering: Process of creating new features from existing data.
67. Annotation: Adding labels or tags to data for supervised learning.
68. Ground Truth: Known correct answers used to train and evaluate models.
69. Accuracy: Proportion of correct predictions among all predictions.
70. Precision: Proportion of true positives among all positive predictions.
🔬 AI Challenges & Considerations
71. Bias: Systematic errors in AI systems that can lead to unfair outcomes.
72. Fairness: Ensuring AI systems treat all individuals and groups equitably.
73. Explainability: Ability to explain how an AI system arrived at its decision.
74. Interpretability: Human understanding of an AI model's decision-making process.
75. Hallucination: When AI generates false or misleading information.
76. Alignment: Ensuring AI systems act in accordance with human values and intentions.
77. Safety: Measures to prevent AI systems from causing harm.
78. Robustness: AI system's ability to perform well under varied or adverse conditions.
79. Privacy: Protection of personal data used by AI systems.
80. Model Drift: Degradation of model performance over time as data patterns change.
🔮 Advanced & Emerging Concepts
81. AGI (Artificial General Intelligence): Hypothetical AI with human-like general intelligence.
82. Federated Learning: Training models across multiple devices while keeping data local.
83. Quantum Machine Learning: Intersection of quantum computing and machine learning.
84. Neuromorphic Computing: Computing architecture inspired by the human brain.
85. Explainable AI (XAI): AI systems designed to be understandable by humans.
86. Synthetic Media: Content created or modified by AI.
87. AI Alignment: Ensuring AI systems' goals align with human values.
88. Foundation Model: Large-scale models trained on broad data that can be adapted to various tasks.
89. Multimodal AI: AI systems that can process and understand multiple types of data.
90. Self-Supervised Learning: AI learning from unlabeled data by generating its own supervision.
🔧 Implementation Strategies
91. Responsible AI: Development and use of AI that is ethical, transparent, and accountable.
92. AI Governance: Frameworks for managing AI risks and ensuring responsible use.
93. Prompt Injection: Security vulnerability where users manipulate AI through carefully crafted inputs.
94. Chain of Thought: Technique for improving reasoning by having AI "think step by step."
95. In-Context Learning: AI's ability to learn from examples provided in the prompt.
96. Temperature: Parameter controlling randomness in AI-generated outputs.
97. Top-k Sampling: Technique for generating diverse AI outputs by selecting from top k options.
98. Nucleus Sampling: Technique for text generation that balances diversity and quality.
99. Model Compression: Techniques to reduce model size while maintaining performance.
100. Evaluating LLMs: Methods for assessing language model performance beyond standard metrics.
🎯 Key Takeaways:
Understanding AI terminology is crucial for effective communication with technical teams
Familiarity with AI concepts helps product managers make informed decisions about AI integration
These terms provide a foundation for deeper exploration of specific AI domains relevant to your products
As AI evolves, continued learning and staying updated on terminology remains important
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