A/B Testing
A/B testing splits traffic between two variants to measure which performs better. A guide to running valid experiments with statistical significance.
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A/B testing splits traffic between two variants to measure which performs better. A guide to running valid experiments with statistical significance.
A multi-step AI process where a model autonomously plans, uses tools, and executes tasks without human input at each step.
An AI agent is a system that uses an LLM to autonomously plan, make decisions, use tools, and take actions to complete a goal.
When an AI model generates confident-sounding but factually incorrect or fabricated information.
The % of new users who reach your product's core value moment within a defined window. The most predictive early-stage metric for long-term retention.
An AI wrapper is a product built on top of a foundation model API with a custom UI, workflow, or niche focus, rather than novel AI model development.
A method of grouping users by a shared trait—typically signup date—and tracking their behavior over time to reveal retention trends.
A feature flag is a code switch that enables or disables a feature at runtime without deploying new code, enabling safer releases and gradual rollouts.
A model offering a permanent free tier alongside paid plans. Works when the marginal cost per free user is low and the upgrade trigger is clear and natural.
Key Performance Indicator - a measurable value that shows how effectively a company is achieving its key business objectives.
A competitive moat is a durable advantage that protects a startup's market position from competitors. Network effects and switching costs are the strongest.
AI models that can process and generate multiple types of data - text, images, audio, and video - within a single system.
An MVP is the simplest version of a product that allows a startup to test its core value hypothesis with real users and gather validated learning.
The North Star Metric is the single number that best captures the core value a product delivers to customers and predicts long-term sustainable growth.
A structured course correction that changes a startup's strategy while preserving validated learning from prior experiments.
Product-led growth is a go-to-market strategy where the product itself drives user acquisition, conversion, and retention without a traditional sales team.
Prompt engineering is the practice of crafting LLM inputs to reliably produce accurate, useful, and correctly formatted outputs for a given task.
The implied cost of future rework created when a team chooses a faster, easier solution today instead of a better long-term approach.
Vertical AI is an AI product built for a specific industry or workflow, combining foundation model capabilities with deep domain expertise and proprietary data.
Also called K-factor: the average number of new users each existing user generates. K above 1 means exponential viral growth; below 1 is partial amplification.
Everything as a Service - the delivery model where any product or capability is offered via subscription over the internet instead of as a one-time purchase.
Learn how to build a product roadmap that drives alignment without stifling adaptability - from prioritization to stakeholder communication.
A practical guide to building your first MVP - how to scope it correctly, what to cut, and how to launch in a way that generates real, actionable learning.
How to build an AI-powered customer support system that deflects 60-80% of tickets while keeping CSAT high.
A step-by-step guide to running customer discovery interviews - who to recruit, what to ask, and how to turn raw conversations into actionable insight.
A practical guide to building AI-native companies: from defining your AI edge to raising capital and scaling your model stack.
A step-by-step guide to building a Retrieval-Augmented Generation system: chunking, embeddings, vector databases, retrieval, and evaluation.
How to pick between GPT-4o, Claude 3.5, Gemini, Llama 3, and Mistral: a decision framework covering cost, context, and task performance.
A framework for selecting AI tools and APIs for your startup stack: benchmarking, cost estimation, vendor risk, and running a time-boxed POC.
Learn how to design, prioritize, and run growth experiments at your startup using the ICE framework, experiment logs, and disciplined test design.
Set up a three-layer analytics stack for your startup - product, revenue, and marketing analytics - and avoid the data traps that waste founder time.
How to run sprint planning in a small startup team—lean, fast, and without the ceremony that makes Scrum feel like a second job.
A practical guide to designing SaaS onboarding that activates users fast, reduces churn, and maximizes the ROI of every signup you earn.
Learn how to write a Product Requirements Document that drives alignment without becoming a bureaucratic burden—with a lean template and real examples.
Learn how to write clear, actionable user stories using the As a / I want / So that format, with acceptance criteria and real examples for product teams.
An iterative software development approach built on the 2001 Agile Manifesto, favoring working software over rigid planning.
A design philosophy that puts AI at the center of the product experience - and the principles that make AI-first products trustworthy and reliable.
A startup built from the ground up with AI as the core product architecture - not a traditional product with AI features added on top.
How product-market fit signals differ for AI products - and why the awe of early demos often masks the absence of real retention.
A human-centered, iterative problem-solving process with five stages: Empathize, Define, Ideate, Prototype, and Test.
Growth loops are self-reinforcing systems where each cycle's output becomes the next cycle's input, generating compounding rather than linear growth.
A design pattern where humans review or approve AI decisions at critical points - balancing automation benefits with accuracy and accountability.
Eric Ries' framework for measuring startup progress using leading indicators when traditional revenue metrics are too early to be meaningful.
A framework for categorizing product features by how they affect customer satisfaction - from basic must-haves to unexpected delighters.
The Lean Startup is a methodology for building products under extreme uncertainty, centered on validated learning and the Build-Measure-Learn feedback loop.
An MLP is the minimum version of a product a user could genuinely love - not just tolerate - balancing learning speed with first impressions.
The AARRR framework breaks startup growth into five measurable stages: Acquisition, Activation, Retention, Revenue, and Referral.
A scoring model for product prioritization using four variables: Reach, Impact, Confidence, and Effort.
The Technology Adoption Lifecycle describes how new technologies spread through a market across five adopter segments, from innovators to laggards.
A two-sided tool that maps your product's features to real customer jobs, pains, and gains - ensuring you build what customers actually need.
The key metrics founders should track for AI products - from AI-specific signals to standard SaaS metrics adapted for AI economics.
When to build custom AI vs buy an off-the-shelf solution - a practical framework for AI infrastructure decisions at each startup stage.
Everyone says 'find PMF' - almost no one explains how. This is the five-stage roadmap from idea to genuine product-market fit, with signals at each step.
Micro-SaaS proves you don't need to raise millions or hire a team to build a valuable software business. Here's why the model works.
Most AI wrapper startups fail within 18 months. Here's the structural reason - and the few ways to build defensibility on top of a foundation model.