Concept library
1134 concepts across 20 domains and 101 tracks. Each track is a coherent sequence — read it top to bottom or dip in wherever the gap is.
All domains
01Foundations
02Transformer Internals
03Training & Fine-Tuning
04Reinforcement Learning
05Inference, Systems & Hardware
06Applied LLM Engineering
07Reasoning, Evaluation & Safety
08Multimodal & Applications
09Classical ML & Statistical Learning
10Causal Inference & Experimentation
11Time Series & Forecasting
12Graphs, Recommenders & Structured Data
13Generative Modelling Beyond Transformers
14Efficiency, Compression & Edge AI
15Search & Information Retrieval
16Data & Feature Engineering
17MLOps & Platform Engineering
18Security, Privacy & Adversarial ML
19Governance, Risk & Responsible AI
20Human-AI Interaction, Product & Economics
20
Human-AI Interaction, Product & Economics
The people using the system, the product decisions around it, and what compute actually costs.
6tracks
43concepts
440cards
5.1hreading
Interaction Design for AI Latency and streaming affordances, error recovery, steering controls, and designing for probabilistic output. 12 concepts · 102 cards
- 01 Accessibility in Streaming and Generated Interfaces Why token streaming fights the assistive-technology mechanism meant to announce it, what a runtime-generated interface does to the possibility of an accessibility review, and where AI output helps and costs disabled users their own voice.
- 02 Citations and Source Attribution in AI Interfaces Why a citation marker in a generated answer is a claim about support that is wrong a measurable fraction of the time, how citation recall and precision quantify that, and why the marker raises trust whether or not it is accurate.
- 03 Designing for Probabilistic Output Why interfaces built on the assumption of correct output fail when output is usually correct, the design moves that make errors survivable, and the cost of hiding uncertainty.
- 04 Latency, Streaming and Perceived Speed Why time to first token dominates perceived speed, how streaming changes what users tolerate, and the interaction costs that come with it.
- 05 Mixed-Initiative Interaction and When to Interrupt The expected-utility rule that decides whether a system should act on its guess, ask the user, or stay silent, why reversibility moves that threshold further than better accuracy does, and what an interruption actually costs.
- 06 Setting Expectations Before First Use Why the direction of a system's errors moves perceived accuracy more than its error rate does, what makes an error boundary learnable, and why onboarding is the cheapest intervention available and the first one to go stale.
- 07 When Chat Is the Wrong Interface Why a text box is the most expressive and least specifiable interface ever shipped, the expressiveness-versus-adjustability tradeoff that explains when to replace it with controls, and what generated UI costs in learnability.
- 08 Branching, Comparison and the Linear Transcript Why a chat transcript makes exploration destructive and comparison expensive, what the research interfaces that break linearity actually provide, and the quadratic comparison cost that stops users exploring long before the model runs out of ideas.
- 09 Feedback Collection That Is Worth Having Why thumbs-up and thumbs-down produce almost no usable signal, which implicit behaviours carry more information, and how to collect explicit feedback that can actually train something.
- 10 Interfaces for Multi-Step Agents What changes when the system acts over minutes rather than responds in seconds, how to make a long trajectory legible without demanding constant attention, and where the intervention points belong.
- 11 Reasoning Traces as an Interface Surface Why the thinking text shown next to an answer is a progress indicator and a steering handle rather than an explanation, how providers split the trace into reasoning and progress updates, and what the faithfulness results mean for anyone rendering it.
- 12 Steering, Correction and Repair What a user does when the output is nearly right, why regeneration is the wrong primary affordance, and the controls that let someone converge rather than resample.
AI Product Management Scoping around uncertainty, quality bars, offline-to-online metric ladders and shipping under model drift. 6 concepts · 65 cards
- 01 Pricing and Packaging an AI Feature Why marginal cost changes the pricing question, the three models in use and what each fails at, and the guardrails a pricing decision needs when usage is heavy-tailed.
- 02 Prototyping AI Features Before the Model Is Ready How Wizard-of-Oz studies, model-in-the-loop prototypes and eval sets written as specifications let a team learn what users need and what the model must do before anyone commits to building it, and the specific ways each method lies.
- 03 Scoping Under Capability Uncertainty Why you cannot specify an AI feature the way you specify software, the cheap experiments that resolve the uncertainty, and the scoping decisions that determine whether a feature is buildable at all.
- 04 Deciding Where the Human Stays The four automation levels available for any decision, the expected-cost calculation that selects between them, and why partial automation is usually right and usually hardest to design.
- 05 Metric Ladders from Offline to Online The chain from a model metric to a business outcome, why each link is weaker than teams assume, and how to validate the links rather than assuming them.
- 06 Shipping Under Model Drift Why an AI product's behaviour changes without a release, what that does to roadmaps and commitments, and the practices that make a product resilient to a dependency that moves on its own.
Trust Calibration & Reliance Over-reliance and automation bias, confidence display, verification cost and human-AI complementarity. 6 concepts · 71 cards
- 01 Appropriate Reliance and Its Two Failures Why the goal is calibrated trust rather than more trust, how over-reliance and under-reliance each destroy the value of a system, and what determines which one a deployment gets.
- 02 Confidence Communication and Calibration What it means for a confidence signal to be calibrated, why models are systematically overconfident, and the forms of communication that help a person rather than a metric.
- 03 Explanations and Their Effect on Reliance Why showing people the reasons behind an AI recommendation often raises acceptance of wrong recommendations as much as right ones, what cognitive forcing and cost-benefit accounts say about the mechanism, and when an explanation actually helps a person catch errors.
- 04 Human-AI Complementarity Why a human-AI team frequently performs worse than the better of its parts, what complementarity requires, and the conditions under which combining actually helps.
- 05 Sycophancy and the Agreement Problem Why preference training produces models that agree with users, the specific behaviours it manifests as, and why it is a trust problem rather than a politeness one.
- 06 The Verification Cost Problem Why an assistant only saves time when checking is cheaper than doing, the tasks where that asymmetry holds and where it inverts, and the design work that creates it.
Human Data & Annotation Guideline design, inter-annotator agreement, preference collection, rater sourcing and label noise. 7 concepts · 74 cards
- 01 Active Learning and Annotation Budgets How choosing which examples to label, by uncertainty or by coverage, can cut annotation cost, why cold starts and sampling bias undermine it, and why the gains that looked large for models trained from scratch often shrink or vanish with pretrained ones.
- 02 Guidelines Are the Model Specification Why the annotation guideline determines what the model learns more than the architecture does, what a usable guideline contains, and the iteration loop that produces one.
- 03 Inter-Annotator Agreement and What It Bounds Why raw agreement overstates reliability, what Cohen's and Krippendorff's coefficients correct for, and why agreement is the ceiling on any model trained from those labels.
- 04 LLM-Assisted Annotation and Label Verification What the evidence says about language models as annotators, why a labeller that is 90% accurate can still produce biased conclusions, and the audit and routing designs that combine cheap model labels with a small human sample into statistically valid results.
- 05 Label Noise and Learning Through It How random and systematic label noise differ in their effect on a model, why memorisation of noisy labels happens late in training, and the techniques that find mislabelled data cheaply.
- 06 Preference Data Collection Why pairwise comparison replaced absolute rating for alignment data, the biases that contaminate it, and the design choices that determine what a reward model actually learns.
- 07 Rater Sourcing, Quality and Welfare The tradeoffs between crowd, vendor, expert and internal annotation, the mechanisms that maintain quality, and the working conditions that shape both the data and the ethics.
Compute Economics Capex versus tokens, utilisation and depreciation, price-performance curves and the cost floor of inference. 6 concepts · 63 cards
- 01 Concentration, Supply and the Compute Market Why AI compute has an unusual supply structure, what the constraints actually are at each layer, and how that shapes strategy for organisations that only want to buy some.
- 02 Energy and Power as the Binding Constraint Why a grid connection measured in megawatts, not a budget measured in dollars, now caps how much AI compute can be built, how PUE converts a power limit into an accelerator count, and why the demand forecasts disagree by a factor of two.
- 03 Training Versus Inference Spend Why inference dominates the lifetime bill for any successful model, the crossover arithmetic, and how that changes which optimisations are worth doing.
- 04 Capex, Depreciation and the Cost of a GPU-Hour How a purchased accelerator's cost becomes an hourly rate, why the depreciation schedule is the contested assumption, and what utilisation does to the answer.
- 05 The Cost Floor of Inference What sets the minimum achievable cost per token, why memory bandwidth rather than compute is the binding constraint, and which techniques move the floor rather than approaching it.
- 06 The Price-Performance Curve Why cost per unit of AI capability has fallen far faster than hardware improvement alone, the three compounding contributions, and what that implies for planning.
AI Diffusion & Labour Adoption measurement, task-level exposure, productivity studies and what the evidence does and does not show. 6 concepts · 65 cards
- 01 Adoption Patterns and What Usage Data Shows What measured usage reveals about where these systems are actually applied, the augmentation-automation split, and why usage concentration differs from exposure predictions.
- 02 Task Exposure Versus Job Replacement Why the unit of analysis is the task rather than the occupation, what exposure measures and what it does not, and the two opposing forces that determine whether exposure becomes displacement.
- 03 General-Purpose Technologies and the Productivity J-Curve Why technologies that eventually transform an economy first depress measured productivity, how unmeasured intangible investment produces a J-shaped path in the statistics, and why the theory is hard to test while you are standing in the dip.
- 04 Measuring Diffusion Honestly Why most claims about AI's economic impact rest on evidence that cannot support them, the hierarchy of evidence quality, and the specific questions to ask of any figure.
- 05 Skill Distribution and Who Benefits The consistent finding that gains concentrate among lower-skilled workers, the competing explanations for it, and the conditions under which the pattern reverses.
- 06 What the Productivity Studies Actually Found The controlled experiments measuring AI's effect on work output, why their results range from large gains to measured slowdowns, and what distinguishes the settings.