Notiky glossary
AI product management glossary
The core language behind Notiky: product brain, decision graph, context packs, and the product memory AI agents need before they ship.
Decision Graph
A decision graph links product decisions to hypotheses, evidence, alternatives, artifacts, and the work that follows.
Product Context
Product context is the durable knowledge behind a product: who it serves, what changed, why decisions happened, and what constraints matter.
Context Pack
A context pack is a bundle of product memory, artifacts, and constraints sent to an AI agent before it starts work.
Hypothesis Tracking
Hypothesis tracking links product assumptions to evidence, decisions, and follow-up work instead of leaving them buried in notes.
How the terms fit together
Product context is the broader knowledge a team needs to make a good choice. A hypothesis captures something the team believes but still needs to test. Evidence from customers or experiments informs a decision. A decision graph connects those choices to their reasons and to the work that follows.
A context pack selects the relevant parts of that knowledge for one agent task. These concepts describe different parts of the same workflow. Keeping them distinct helps people see whether a statement is a fact, an assumption, an accepted choice, or an instruction for a specific piece of work.
Use the glossary with a real task
Consider a team improving onboarding. Its product context describes new users and their goals. Its hypothesis predicts that a clearer first action will help them begin. Research provides evidence; the decision identifies what to change; the context pack explains that change to the agent implementing it.
Each definition includes a practical example and guidance on what to record. Read product context first for an overview, then follow the links to decisions, hypotheses, and context packs. Revisit the definitions when a brief feels ambiguous: the missing piece is often the distinction between what is known and what is still being tested.