Product, dev, and AI terms. Simply explained.
Plain-English definitions of the words you'll actually run into when building a startup.
Before you write a single line of code, you need to understand what you're building and why. These are the product and strategy terms that help you define scope, validate demand, and make decisions that keep your build focused and fundable.
A PoC proves technical feasibility. An MVP proves market demand.
A prototype demonstrates an idea. An MVP validates it with real users.
A focused session to define exactly what to build before writing any code.
An MVP built to handle real users, real data, and real traffic from day one.
The smallest functional product that solves a real problem for real users.
A development model where the full cost is agreed upfront before work begins.
A methodology for building products through rapid experimentation and validated learning.
The process of designing, building, and shipping a Minimum Viable Product.
The point where a product satisfies strong, growing market demand.
The future cost of shortcuts taken in code today.
From your first technical conversation with a developer to your first production deploy, these are the terms that will come up constantly. Understanding them helps you ask better questions, avoid costly mistakes, and stay in control of what gets built.
A structured system for storing, organising, and retrieving the data your product depends on.
A two-sided platform that connects buyers and sellers within a single product.
The combination of technologies used to build and run a product.
A low-fidelity layout showing the structure and flow of a screen before visual design.
An iterative approach to software development focused on short cycles and continuous feedback.
A set of rules that lets two software systems communicate with each other.
The system that verifies who a user is before giving them access to a product.
Building the server-side logic, APIs, and database layer that power your product.
The process of releasing a software application to a live environment where users can access it.
Building the parts of a product that users see and interact with directly.
Building both the frontend and backend of a product within the same team or developer.
Software delivered over the internet and paid for on a subscription basis.
AI has changed how products are built, how fast teams move, and what's now possible on a startup budget. These are the models, frameworks, and workflows every founder should understand before deciding how to use AI in their product.
A database optimised for storing and searching AI-generated embeddings.
Adding AI capabilities to a product by connecting to AI models and services via API.
A large language model, an AI system trained to understand and generate text.
Further training a pre-built AI model on your specific data to specialise its behaviour.
The practice of designing inputs to an AI model to get consistently useful outputs.
Retrieval-Augmented Generation: giving an AI model access to your specific data to answer questions accurately.
Using AI to generate code through natural language prompts without deep engineering oversight.
Using software to automatically execute repetitive tasks and processes without manual intervention.