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Word Embeddings: Turning Words into Vectors
Word embeddings turn words into numerical vectors, like coordinates on a map of meaning. Words with similar meanings, like "king" and "queen," are placed close together in this vector space. This is fundamental for text analysis in machine learning, allowing models to grasp semantic relationships instead of just matching text. The footgun is assuming the vector's individual numbers are human-interpretable; they are abstract features learned from data.
Byte Pair Encoding: Compressing Text for LLMs
Think of Byte Pair Encoding (BPE) as creating custom abbreviations for common letter pairs to compress text. It repeatedly finds the most frequent pair, like 'th', and merges it into a new token. LLMs use this to build vocabularies of common sub-word units, helping them understand rare words. The main footgun is that the final vocabulary size is fixed; choosing the wrong size can hurt model performance and efficiency.
Large Language Models (LLMs)
A large language model is a sophisticated pattern-matching engine trained on a massive library of text. They power modern chatbots and can generate, summarize, or translate text by predicting the most probable next word based on the patterns they've learned. The key footgun is that their output reflects the biases and inaccuracies of their training data, making them confident but potentially unreliable.
UX Research: Building for Real People, Not Assumptions
UX Research is about watching what people *do*, not just what they *say*, to build products that solve their actual problems. It's used to design everything from websites to medical devices, guiding development with real user feedback.
Double Diamond: Explore, Then Focus, Twice
The Double Diamond model prevents building the wrong thing by forcing two cycles of 'go wide, then narrow down.' First, explore the problem space, then define a specific problem. Second, explore solutions, then deliver a tested one.
Jobs to Be Done: Sell the Hole, Not the Drill
The Jobs to Be Done framework says customers 'hire' products to get a job done. Instead of selling a drill, sell the quarter-inch hole. This reframes innovation around stable customer needs, not temporary product features.
Formative vs. Summative Evaluation: Improve vs. Judge
Formative evaluation improves a design in progress; summative evaluation judges a finished one. Use formative tests to find and fix flaws iteratively. Use summative tests to measure a shipped product against a benchmark, like a prior version or a competitor.
UX Ethics: A Professional Code of Conduct
UX ethics formalizes your responsibility to users. It's about centering human well-being, being transparent with data, and actively preventing harm. This guides decisions from research to launch.
Triangulation: Stronger UX Insights from Multiple Angles
Triangulation strengthens research by combining methods to cover each one's blind spots. For example, pair a quantitative study showing *what* users do with a qualitative one explaining *why*. The footgun is treating a single data source as definitive proof.
UX Research Repository: Your Team's Shared Brain
A research repository is your team's shared brain for user insights, preventing knowledge from getting lost. It centralizes reports, recordings, and notes, making them searchable to prevent duplicate studies. The main footgun is poor adoption.
Debriefing: Closing the Loop in Research
Debriefing is a structured conversation after a research session to gather feedback and clarify observations. Use it after usability tests to understand a user's thought process and answer their questions.
Data Minimization: Collect Only What You Need
Treat user data like a liability, not an asset. The Data Minimization principle states you should only collect personal data that is strictly necessary for a specific purpose. This is a core tenant of GDPR. The footgun is collecting data "just-in-case".
Securing Research Data with a Management System
Treat data security as a living system for managing risk, not a one-off checklist. A framework like ISO 27001 helps you systematically protect sensitive research data by defining policies and controls.
Institutional Review Board (IRB): Your Research Ethics Checkpoint
An IRB is an ethics committee that acts as a mandatory checkpoint for research involving human subjects. Before running user studies or surveys, especially in academic or medical settings, you must get their approval.
GDPR for UX Research: Beyond the Consent Form
GDPR forces you to treat user data with respect: collect only what you need for a specific purpose and keep it safe. It applies to all research involving personal data from EU residents. The biggest footgun is collecting data "just in case."
Deception: The Core Concept
Deception is the act of convincing someone of untrue information that the sender knows is false. It's often done for personal gain and relies on the receiver's lack of knowledge about the truth.
UX Research Questions: The 'Why' Before the 'What'
A research question is your study's North Star—the high-level "why" you're conducting research, not a question you ask users. Before writing an interview guide, define these to keep your study focused.
Research Objectives: From 'What If' to 'What to Test'
Research objectives translate vague business worries into specific, testable questions about user behavior. They are the blueprint for your study, ensuring you test what matters.
Research Hypothesis: A Testable Bet on Reality
A research hypothesis is a testable bet about user behavior, not just a guess. It frames A/B tests by turning an observation into a statement you can prove or disprove. The footgun is confusing it with a theory; a hypothesis is a starting point, not.
Screener Surveys: Your Filter for Valid User Research
A screener survey is your filter for finding the right research participants. It ensures your study includes representative users, not just anyone, by asking questions about their behaviors and demographics.