Concept library
883 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
30concepts
354cards
3.5hreading
Interaction Design for AI Latency and streaming affordances, error recovery, steering controls, and designing for probabilistic output. 5 concepts · 58 cards
- 01 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.
- 02 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.
- 03 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.
- 04 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.
- 05 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. 5 concepts · 58 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 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.
- 03 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.
- 04 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.
- 05 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. 5 concepts · 64 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 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.
- 04 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.
- 05 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. 5 concepts · 60 cards
- 01 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.
- 02 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.
- 03 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.
- 04 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.
- 05 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. 5 concepts · 56 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 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.
- 03 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.
- 04 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.
- 05 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. 5 concepts · 58 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 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.
- 04 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.
- 05 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.