AI Product Glossary
AI Product Glossary
Simple definitions for founders building AI products. No heavy jargon, no hype, just the terms you need to understand before you build.
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AI Agent
An AI agent is a software system that works toward a goal by using a model to reason, make decisions, and take actions, often with access to tools or external systems. Compared with a basic chatbot, an agent is more goal-driven and action-oriented.
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AI Assistant
An AI assistant is an intelligent application that understands natural-language requests and helps a user by answering questions, completing tasks, or guiding them through work. It is usually conversational and waits for user instructions rather than acting with broad autonomy.
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AI Discovery
At Sprint966, AI Discovery means the early product-discovery stage where the team clarifies the problem, validates user needs, and decides whether AI actually belongs in the solution before building starts. It is an AI-specific version of standard product discovery and scope definition.
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AI Knowledge Base
An AI knowledge base is a knowledge system that uses AI to find, surface, and answer from trusted company content instead of relying only on a model’s built-in memory. In practice, it combines organized knowledge with retrieval or search so answers stay tied to approved sources.
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AI MVP
An AI MVP is the simplest usable version of an AI product that lets a team test the idea, learn from real users, and improve without building the full platform first. The goal is learning and validation, not feature completeness.
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AI Product
An AI product is a software product that uses artificial intelligence to perform tasks such as understanding language, learning from data, making predictions, or generating outputs. At Sprint966, this is the founder-friendly label for an AI-powered application or first product idea.
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AI Product Scope
At Sprint966, AI product scope means the clear boundary of what the first version of the AI product will include, exclude, deliver, and delay. It is the AI-focused version of standard project or product scope definition.
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AI UX/UI
AI UX/UI is the design of interfaces and user experiences for AI-powered products, including how people give input, understand outputs, build trust, and stay in control. Good AI UX/UI treats AI as part of the user experience, not just a model behind the screen.
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AI Workflow Automation
AI workflow automation uses AI inside a business process to help move work from input to output, often by classifying, extracting, routing, generating, or reviewing information along the way. It goes beyond simple rule-based automation by adding learning, language understanding, or adaptive decision support.
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Autonomous Agent
An autonomous agent is an AI agent that can plan, act, and adapt toward a goal with limited or no continuous human intervention. It is more independent than a standard assistant because it can choose steps and carry work forward on its own.
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Build vs Cut
At Sprint966, Build vs Cut is a scoping rule for deciding which features belong in the first version and which ones should be removed or delayed. It is based on standard product prioritization and clear scope boundaries, but expressed in simpler founder language.
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Clarity Before Code
At Sprint966, Clarity Before Code means defining the problem, scope, priorities, and first version before development begins. It reflects the product practice of understanding needs and validating direction before building solutions.
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Custom AI Model
A custom AI model is a model that is trained, tuned, or built for a specific business use case instead of using only a generic prebuilt model. It is usually created when the product needs specialized behavior, data handling, or output quality that standard models cannot provide on their own.
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Document AI
Document AI is AI that reads, understands, classifies, and extracts information from documents such as PDFs, forms, invoices, and scans. Its job is to turn document content into structured, usable data.
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Fine-Tuning
Fine-tuning is the process of taking a pretrained model and adapting it with task-specific examples so it performs better on a narrower use case. It improves specialization without training a model from scratch.
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First Version
At Sprint966, First Version is the plain-language term for the smallest usable release of a product that is worth testing with real users. It is essentially the founder-friendly way to talk about an MVP without requiring startup jargon.
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Founder Decision System
At Sprint966, Founder Decision System is a plain-language label for the process of turning a broad product idea into a focused first build through prioritization, tradeoffs, and scope decisions. It is not a universal industry term; it is Sprint966’s visual way of describing structured product decision-making.
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Grounded Answers
Grounded answers are AI responses that stay tied to verifiable source material instead of guessing or inventing unsupported claims. In practice, grounding improves reliability by connecting the model’s output to trusted context, documents, or live data.
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Human-in-the-Loop
Human-in-the-loop is an AI design pattern where a person reviews, guides, corrects, or approves part of the system’s work. It is used when human judgment is still needed for quality, safety, accountability, or high-stakes decisions.
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Product Roadmap
A product roadmap is a strategic plan that shows a product’s direction, priorities, and progress over time. It helps founders and teams understand what is being built now, what comes next, and what can wait.
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Prompt-to-Output Flow
At Sprint966, Prompt-to-Output Flow means the full user path from what a person asks an AI system to how the system responds and how the user can refine that result. It is based on prompt design plus output design, not just the model’s raw answer.
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RAG
RAG stands for Retrieval-Augmented Generation. It is a method where an AI system retrieves relevant information from external sources first, then uses that information to generate a better answer.
Sources
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Retrieval-Augmented Generation
Retrieval-Augmented Generation is an AI pattern that extends a language model by grounding its responses in retrieved content such as company documents, databases, or knowledge bases. It is commonly used when answers must reflect trusted or up-to-date information, not only the model’s training data.
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Scope Creep
Scope creep happens when a project keeps expanding beyond the original plan, usually through extra requirements, tasks, or features added without proper review. The result is often delays, more cost, and a harder product to finish well.
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Smart Rules AI Product
At Sprint966, a Smart Rules AI Product is a product that solves the first version of the problem with clear rules, scoring, logic, or decision trees instead of starting with a more complex generative AI system. It is useful when the logic is already known and the goal is making a focused decision, not generating open-ended answers.
Definitions are written in simple language for founders. Sprint966-specific terms are based on common product strategy, scoping, and AI product design concepts.
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