Here is a list of 60 examples of modeling, categorized by their primary field, with a simple explanation and an example prompt for each.
Models are simplified representations of a system or process. They are used to understand, predict, or analyze complex problems in every field, from science and business to art and computer science.
🧠 I. Machine Learning & AI Models
These models are algorithms trained on data to find patterns, make predictions, or classify information.
Linear Regression
Explanation: A statistical model used to predict a continuous value (e.g., price, temperature) based on one or more input variables, assuming a linear relationship.
Example Prompt: "Analyze this dataset of house sizes and their final sale prices to build a model that predicts a house's price based on its square footage."
Logistic Regression
Explanation: A statistical model used for binary classification. It predicts the probability of an outcome being in one of two categories (e.g., yes/no, spam/not spam).
Example Prompt: "Using this data of customer age, income, and purchase history, create a model to predict whether a new customer will make a purchase (yes/no)."
k-Nearest Neighbors (KNN)
Explanation: A simple algorithm that classifies a new data point based on the "majority vote" of its 'k' nearest neighbors in the training data.
Example Prompt: "Build a model that classifies a new iris flower as 'setosa,' 'versicolor,' or 'virginica' based on the species of the 5 most similar flowers in our dataset."
Decision Tree
Explanation: A flowchart-like model where each internal node represents a "test" on a feature (e.g., "Is age > 30?"), each branch is an outcome, and each leaf node is a class label.
Example Prompt: "Create a model to decide if a bank should approve a loan. Use data on applicant income, credit score, and loan amount to build a set of simple 'yes/no' rules."
Random Forest
Explanation: An "ensemble" model that builds many individual decision trees during training and outputs the class that is the mode (classification) or mean (regression) of the individual trees. It's more accurate and robust than a single tree.
Example Prompt: "Develop a high-accuracy model to predict which customers are at high risk of 'churning' (leaving the service) by combining the predictions of hundreds of small decision trees."
Support Vector Machine (SVM)
Explanation: A classification model that finds the "hyperplane" (a line or plane) that best separates data points of different classes in a high-dimensional space.
Example Prompt: "Given a dataset of medical images with features for tumor size and cell shape, build a model that draws the optimal boundary to separate 'malignant' from 'benign' cases."
Naive Bayes Classifier
Explanation: A probabilistic classifier based on Bayes' Theorem. It's "naive" because it assumes all features are independent of each other, which works surprisingly well for tasks like text classification.
Example Prompt: "Build a spam filter. Using a labeled dataset of emails, calculate the probability that an email is 'spam' given the presence of words like 'viagra,' 'free,' and 'money.'"
k-Means Clustering
Explanation: An unsupervised learning algorithm that groups a dataset into 'k' number of clusters, where each data point belongs to the cluster with the nearest mean (center).
Example Prompt: "Analyze this list of e-commerce customers based on their spending habits and frequency of visits. Group them into 3 distinct segments so we can create targeted marketing."
DBSCAN (Density-Based Spatial Clustering)
Explanation: An unsupervised clustering algorithm that groups together points that are closely packed, marking points that lie alone in low-density regions as outliers. It's excellent for finding arbitrarily shaped clusters.
Example Prompt: "Analyze this map of reported crimes. Instead of making simple circles, find the dense, oddly-shaped 'hotspots' of criminal activity and identify isolated incidents as outliers."
Artificial Neural Network (ANN)
Explanation: A model inspired by the human brain, composed of layers of interconnected "neurons." It learns by adjusting the connection strengths (weights) to map inputs to desired outputs.
Example Prompt: "Create a foundational AI model that can learn to recognize handwritten digits (0-9) from a database of 60,000 images."
Convolutional Neural Network (CNN)
Explanation: A specialized type of neural network designed for processing grid-like data, such as images. It uses "convolutional" layers to automatically learn features like edges, textures, and shapes.
Example Prompt: "Build an image recognition model that can classify photos as containing a 'cat,' 'dog,' or 'bird.' The model must be able to detect these animals regardless of their position or scale in the photo."
Recurrent Neural Network (RNN)
Explanation: A type of neural network with internal memory, making it ideal for sequential data like text, speech, or time-series data. It processes inputs one at a time while remembering previous inputs.
Example Prompt: "Develop a model that can predict the next word in a sentence. Use a large text corpus to train it on the sequences and patterns of human language."
Gradient Boosting
Explanation: An ensemble technique that builds models (typically trees) one at a time, where each new model corrects the errors of the previous ones. It is a highly accurate and popular model.
Example Prompt: "Build a top-performing model to predict airline flight delays. Sequentially add rules, with each new rule focusing on fixing the prediction mistakes made by the previous rules."
📈 II. Business & Financial Models
These models are frameworks used to make decisions, analyze performance, and understand market dynamics.
SWOT Analysis
Explanation: A strategic framework that identifies an organization's internal Strengths and Weaknesses, as well as its external Opportunities and Threats.
Example Prompt: "Perform a SWOT analysis for a local coffee shop competing with a new Starbucks opening nearby."
PESTLE Analysis
Explanation: A strategic tool for scanning the external macro-environment. It analyzes Political, Economic, Social, Technological, Legal, and Environmental factors.
Example Prompt: "Analyze the external factors an electric car company must consider before expanding into the European market using the PESTLE framework."
Porter's Five Forces
Explanation: A model for analyzing an industry's competitive landscape. The five forces are: threat of new entrants, threat of substitutes, bargaining power of buyers, bargaining power of suppliers, and rivalry among existing competitors.
Example Prompt: "Use Porter's Five Forces to analyze the long-term profitability and competitive intensity of the smartphone industry."
Ansoff Matrix
Explanation: A 2x2 matrix that helps businesses plan their growth strategies: Market Penetration (existing product, existing market), Product Development (new product, existing market), Market Development (existing product, new market), and Diversification (new product, new market).
Example Prompt: "A software company wants to grow. Use the Ansoff Matrix to lay out their four main strategic options, from selling more of their current product to creating something new for a new audience."
Business Model Canvas
Explanation: A one-page visual chart with nine blocks (e.g., Key Partners, Value Proposition, Customer Segments, Revenue Streams) that describes how an organization creates and delivers value.
Example Prompt: "Map out the entire business model for a subscription box service using the Business Model Canvas."
BCG Matrix
Explanation: A portfolio management matrix that classifies a company's business units as Stars (high growth, high share), Cash Cows (low growth, high share), Question Marks (high growth, low share), or Dogs (low growth, low share).
Example Prompt: "A corporation owns a streaming service, a movie studio, a theme park, and a retail chain. Classify each of these units using the BCG Matrix to decide where to invest, harvest, or divest."
Balanced Scorecard
Explanation: A strategic performance management tool that tracks metrics across four perspectives: Financial, Customer, Internal Business Processes, and Learning & Growth.
Example Prompt: "Design a performance dashboard for a hospital that goes beyond just financial numbers. Include metrics for patient satisfaction, ER wait times, and employee training."
GE-McKinsey Nine-Box Matrix
Explanation: A portfolio analysis tool that plots business units on a 3x3 grid based on their "Industry Attractiveness" and "Business Unit Strength." It helps prioritize investment.
Example Prompt: "Analyze a tech company's portfolio of products (e.g., cloud computing, hardware, advertising) using the GE-McKinsey matrix to determine which units to 'Invest,' 'Hold,' or 'Harvest.'"
AIDA Model
Explanation: A marketing model describing the four stages of a customer's journey: Attention (or Awareness), Interest, Desire, and Action.
Example Prompt: "Describe the marketing funnel for a new video game, explaining how a customer goes from first seeing a trailer (Attention) to buying the game (Action)."
The 4Ps of Marketing
Explanation: A foundational marketing mix model: Product (what you sell), Price (how much you charge), Place (where you sell it), and Promotion (how you advertise it).
Example Prompt: "Define the marketing mix (the 4Ps) for a new brand of vegan protein bars."
Discounted Cash Flow (DCF) Model
Explanation: A financial valuation model that estimates an investment's value by projecting its future cash flows and "discounting" them to their present value.
Example Prompt: "Create a 5-year DCF model to determine the current intrinsic value of Company XYZ, given its projected revenues and a discount rate of 8%."
Leveraged Buyout (LBO) Model
Explanation: A financial model used to analyze a scenario where a company is acquired using a significant amount of borrowed money (debt), with the assets of the company being acquired used as collateral.
Example Prompt: "Build an LBO model for a private equity firm's potential acquisition of a stable, cash-generating manufacturing company."
🔬 III. Scientific & Conceptual Models
These models are theoretical or conceptual constructs used to explain complex phenomena in the natural world.
Bohr Model of the Atom
Explanation: A conceptual model (now outdated but foundational) depicting the atom as a small, positively charged nucleus surrounded by electrons orbiting in specific, quantized energy shells.
Example Prompt: "Draw a simple diagram of a nitrogen atom based on the Bohr model, showing the correct number of electrons in each energy shell."
Watson-Crick DNA Model
Explanation: The physical and conceptual model of DNA's structure as a "double helix," with two strands of nucleotide base pairs (A-T, G-C) twisted around each other.
Example Prompt: "Explain how genetic information is stored and replicated using the Watson-Crick double helix model as your basis."
The Water Cycle (Hydrologic Cycle)
Explanation: A conceptual model showing the continuous movement of water on, above, and below the Earth's surface, including the processes of evaporation, condensation, precipitation, and collection.
Example Prompt: "Create a diagram of the water cycle, labeling all its key processes and showing how water moves from the ocean to the atmosphere and back to the land."
The Carbon Cycle
Explanation: A conceptual model describing how carbon atoms continuously travel between the atmosphere, oceans, land, and living organisms (e.g., through photosynthesis and respiration).
Example Prompt: "Explain the role of forests and oceans in the global carbon cycle, and describe how burning fossil fuels disrupts this model."
Food Web Model
Explanation: A conceptual model showing the complex network of feeding relationships within an ecosystem, illustrating the flow of energy from producers to consumers and decomposers.
Example Prompt: "Create a food web model for a North American forest, including at least three producers, five primary consumers, and two apex predators."
Plate Tectonics Model
Explanation: The scientific theory and conceptual model that describes the large-scale motion of the Earth's lithosphere, which is broken into rigid "tectonic plates."
Example Prompt: "Use the model of plate tectonics to explain how the Himalayan mountains were formed and why earthquakes are common in Japan."
Particle Model of Matter
Explanation: A conceptual model that describes all matter (solids, liquids, gases) as being composed of tiny, constantly moving particles. The state of matter depends on the energy and spacing of these particles.
Example Prompt: "Using the particle model of matter, explain what happens to water molecules when an ice cube melts and then boils into steam."
The Standard Model of Particle Physics
Explanation: The scientific theory and mathematical model that describes all known fundamental forces (except gravity) and classifies all known elementary particles (e.g., quarks, leptons, bosons).
Example Prompt: "Explain the basic components of the Standard Model of Particle Physics, identifying the particles that make up a proton and the particle responsible for the electromagnetic force."
VSEPR Model (Chemistry)
Explanation: The Valence Shell Electron Pair Repulsion model is used to predict the 3D geometry of individual molecules based on the number of electron pairs surrounding their central atoms.
Example Prompt: "Using the VSEPR model, predict and draw the molecular shape of a water molecule (H₂O) and a methane molecule (CH₄)."
Mental Model (Psychology)
Explanation: A conceptual model of how something works in the real world, which an individual carries in their mind. These models shape our understanding and actions.
Example Prompt: "Describe the 'mental model' a new user might have for how a 'cloud storage' service like Google Drive works. What misconceptions might they have?"
🧮 IV. Mathematical & Statistical Models
These models use mathematical formulas and statistical assumptions to describe and predict behavior.
SIR Model (Epidemiology)
Explanation: A mathematical model that divides a population into three "compartments" to predict the spread of an infectious disease: Susceptible, Infectious, and Recovered.
Example Prompt: "Create a simple SIR model to show how a new virus might spread through a population of 100,000, given an infection rate of 0.2 and a recovery rate of 0.1."
Lotka-Volterra (Predator-Prey) Model
Explanation: A pair of differential equations that describe the population dynamics of two species interacting, one as a predator and one as its prey.
Example Prompt: "Model the population cycles of rabbits (prey) and foxes (predators) over 50 years, showing how their populations rise and fall in relation to each other."
Black-Scholes Model
Explanation: A mathematical model used in finance to determine the theoretical price of European-style options, based on variables like the asset's price, strike price, time to expiration, and volatility.
Example Prompt: "Calculate the fair price of a call option for stock ABC, which trades at $100, with a strike price of $105, expiring in 30 days, given a 20% volatility and a 5% risk-free rate."
Normal Distribution (Bell Curve)
Explanation: A statistical model for a continuous probability distribution where data clusters around a central mean. It's used to model natural phenomena like height, IQ, and measurement errors.
Example Prompt: "Assume student SAT scores are modeled by a normal distribution with a mean of 1000 and a standard deviation of 200. What percentage of students score above 1200?"
Poisson Distribution
Explanation: A statistical model for probability that describes the number of events occurring in a fixed interval of time or space, given a known average rate.
Example Prompt: "A call center receives an average of 10 calls per hour. Use a Poisson model to calculate the probability of receiving exactly 0 calls in the next hour."
Markov Chain
Explanation: A mathematical model that describes a sequence of possible events where the probability of each event depends only on the state attained in the previous event (it is "memoryless").
Example Prompt: "Model a simple weather system as a Markov Chain, where a 'Sunny' day has a 70% chance of being followed by another 'Sunny' day and a 30% chance of a 'Rainy' day. What is the long-term probability of rain?"
Linear Programming Model
Explanation: A mathematical method for finding the best possible outcome (e.g., maximum profit, minimum cost) in a model whose requirements are represented by linear relationships.
Example Prompt: "A factory makes tables and chairs. Tables yield $50 profit and chairs $30. A table takes 2 hours of carpentry and 1 hour of finishing. A chair takes 1 hour of each. Given 100 carpentry hours and 80 finishing hours, model this as a linear program to find the mix of products that maximizes profit."
Game Theory (e.g., Prisoner's Dilemma)
Explanation: A mathematical model of strategic interaction between rational decision-makers. The Prisoner's Dilemma, specifically, shows why two individuals might not cooperate, even if it appears to be in their best interest.
Example Prompt: "Set up a payoff matrix for two competing companies deciding whether to 'Advertise' or 'Not Advertise,' where advertising steals customers but costs money. Analyze this as a Prisoner's Dilemma."
Ising Model (Physics)
Explanation: A mathematical model in statistical mechanics used to describe ferromagnetism. It consists of a grid of "spins" (up or down) that influence each other, providing a simple model for how collective, large-scale behavior (like a magnet) emerges from simple, local interactions.
Example Prompt: "Simulate a 2D Ising model to show how a grid of random magnetic spins aligns into a single domain as the 'temperature' of the system is lowered."
💻 V. Computational & Computer Science Models
These are abstract models used to design algorithms, systems, and simulations.
Finite Element Analysis (FEA)
Explanation: A computational model that breaks down a large, complex physical object (like a bridge or car chassis) into a "mesh" of millions of small, simple "finite elements." It then solves the physics equations for each element to simulate stress, heat, or fluid flow for the entire object.
Example Prompt: "Run an FEA simulation on this 3D model of a new bicycle frame to find the points of maximum stress when a 200-pound rider hits a pothole."
Monte Carlo Simulation
Explanation: A computational model that uses repeated random sampling to obtain numerical results. It's used to model the probability of different outcomes in a process that cannot easily be predicted due to random variables.
Example Prompt: "Run a Monte Carlo simulation (10,000 trials) to forecast a project's completion date, given that task A takes (3-5 days), task B takes (2-7 days), and task C takes (4-5 days), all with random probabilities."
Agent-Based Model (ABM)
Explanation: A computational model that simulates the actions and interactions of autonomous "agents" (e.g., people, cars, cells) with a set of rules, and observes the emergent, large-scale behavior of the system.
Example Prompt: "Create an agent-based model of a city evacuation. Give 10,000 'car' agents a simple rule (e.g., 'take the nearest highway') to see how traffic jams form organically."
Conway's Game of Life (Cellular Automaton)
Explanation: A computational model (and zero-player game) consisting of a 2D grid of cells, each either "alive" or "dead." The state of each cell in the next generation is determined by a simple set of rules based on its 8 neighbors. It demonstrates how complex, "living" patterns can emerge from simple rules.
Example Prompt: "Simulate Conway's Game of Life on a 100x100 grid, starting with a random 'seed' of living cells, and observe the patterns that emerge over 500 generations."
Turing Machine
Explanation: A theoretical model of computation. It consists of a tape, a head that can read/write symbols, and a set of rules. It is powerful enough to simulate any computer algorithm, and it's used to define the very limits of what is computable.
Example Prompt: "Describe the components and rules for a simple Turing Machine that can determine if a string of 1s and 0s contains an even number of 1s."
Finite Automaton (Finite State Machine)
Explanation: A simple abstract model of computation that can be in one of a finite number of "states" at any given time. It's used to design simple algorithms, text parsers, and hardware circuits.
Example Prompt: "Design a finite state machine for a vending machine that starts in a 'Locked' state, moves to an 'Unlocked' state after 50 cents is inserted, and returns to 'Locked' after dispensing a product."
Relational Model (Databases)
Explanation: The dominant conceptual model for databases. It organizes data into "relations," which are tables consisting of rows (tuples) and columns (attributes). Data is linked between tables using "keys."
Example Prompt: "Design a relational database schema for a university, with separate tables for 'Students,' 'Courses,' and 'Enrollment' that links them."
Graph Model (Databases)
Explanation: A model that represents data as a set of "nodes" (entities) and "edges" (relationships). It's optimized for traversing complex relationships, like in social networks or recommendation engines.
Example Prompt: "Model a social network using a graph database, where 'People' are nodes and an 'FRIENDS_WITH' edge connects them."
🏛️ VI. Physical & Visual Models
These are tangible, visual, or three-dimensional representations of an object or system.
Architectural Scale Model
Explanation: A physical, 3D representation of a building or group of buildings, built to a specific ratio (e.g., 1:100) to communicate design, layout, and spatial relationships.
Example Prompt: "Build a 1:50 scale physical model of this proposed two-story house, using foam core and balsa wood to show the interior layout and exterior design."
Prototype (Product Design)
Explanation: An early, functional (or non-functional) model of a new product. It can range from a paper sketch (low-fidelity) to a 3D-printed, working assembly (high-fidelity) used for testing and iteration.
Example Prompt: "Create a low-fidelity paper prototype of our new mobile app's user interface so we can test the user flow before writing any code."
Wind Tunnel Model
Explanation: A scaled-down physical model of an object (e.g., car, airplane, building) that is placed in a wind tunnel to study the effects of air moving around it (aerodynamics).
Example Prompt: "Construct a 1:20 scale model of this new Formula 1 car design for wind tunnel testing to analyze its aerodynamic drag and downforce."
Orrery
Explanation: A physical, mechanical model of the solar system that illustrates the relative positions and motions of the planets and moons, typically driven by a clockwork mechanism.
Example Prompt: "Design a mechanical orrery that accurately shows the orbital periods of Earth, Mars, and Jupiter relative to each other."
Geological Cross-Section
Explanation: A 2D visual model (a "slice" through the earth) that shows the vertical arrangement of rock layers (strata), faults, and other geological features.
Example Prompt: "Draw a geological cross-section of this valley, showing the anticline fold in the limestone and the fault line that displaces the shale layer."
Flowchart
Explanation: A visual diagram that models a process, algorithm, or workflow. It uses standard shapes (e.g., ovals for start/end, rectangles for processes, diamonds for decisions) connected by arrows.
Example Prompt: "Create a flowchart that models the step-by-step process of a customer ordering a pizza online, from logging in to receiving a confirmation email."
Wireframe (UI/UX Design)
Explanation: A low-fidelity, visual model of a website or app's user interface. It focuses purely on structure, layout, and content placement, intentionally ignoring colors and graphics.
Example Prompt: "Draw a set of wireframes for the 'Home' screen and 'Checkout' page of a new e-commerce mobile app."
Mind Map
Explanation: A visual diagram used to model and organize information. It starts with a central concept, and related ideas, words, and tasks radiate outwards in a hierarchical, tree-like structure.
Example Prompt: "Create a mind map to brainstorm all the key topics and sub-topics you need to cover in a research paper about renewable energy."
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Certainly! Let's dive deeper into the Machine Learning & AI Models category.
This field is vast, but the models can generally be grouped by their learning style and purpose.
🧠 I. Supervised Learning (Learning from Labeled Data)
In supervised learning, the model is a "student" given a "textbook" (the training data) full of questions (inputs) and the correct answers (outputs/labels). The model's goal is to learn the general rules to answer questions it has never seen before.
A. Regression Models (Predicting a Number)
These models predict a continuous value, like a price, temperature, or score.
Linear Regression: This is the simplest regression model. It finds the single best-fit straight line that describes the relationship between the inputs and the output.
Deeper Dive: The model calculates a "slope" (coefficient) for each input feature, which tells you how much the output is expected to change for a one-unit increase in that feature. For example, in a housing price model, the "square footage" feature might have a coefficient of 150, meaning the model predicts that each additional square foot adds $150 to the price, all else being equal.
B. Classification Models (Predicting a Category)
These models predict a discrete class label, like "spam" or "not spam," "cat" or "dog," or "approved" or "denied."
Decision Tree: This model builds a flowchart of yes/no questions to arrive at a decision.
Deeper Dive: To decide which question to ask first (e.g., "Is income > $50k?" or "Is credit score > 700?"), the algorithm picks the question that best splits the data into "pure" groups. It measures this using concepts like "Gini impurity" or "entropy." A "pure" group would be one where all the data points belong to a single class (e.g., "all approved").
Support Vector Machine (SVM): This model finds the "best" line or plane to separate two classes.
Deeper Dive: The "best" line is the one that has the largest margin—the maximum possible distance—between itself and the nearest data points from each class. These nearest points are called the "support vectors," and they are the only points that matter in defining the boundary. This focus on the boundary points makes SVMs very effective, especially when the data isn't perfectly separable.
🌀 II. Unsupervised Learning (Finding Hidden Patterns)
In unsupervised learning, the model is given a dataset without any labels or correct answers. Its job is to find the hidden structure, patterns, or groupings on its own.
A. Clustering Models (Grouping Similar Data)
k-Means Clustering: This model groups data into 'k' number of clusters.
Deeper Dive: The algorithm is iterative:
Place: It starts by randomly placing 'k' cluster centers (centroids) on the data.
Assign: It assigns every data point to its nearest centroid.
Move: It moves each centroid to the average position of all the points assigned to it.
Repeat: It repeats steps 2 and 3 until the centroids stop moving, resulting in final, stable clusters.
DBSCAN: This model groups data based on density rather than a central point. It's fantastic for finding clusters that have strange, non-circular shapes (like a crescent or a spiral) and for automatically identifying outliers.
B. Dimensionality Reduction (Simplifying Data)
(New Model) Principal Component Analysis (PCA): This is a technique used to reduce the number of features (columns) in a dataset while losing as little information as possible.
Explanation: Imagine a 3D cloud of data points shaped like a flat pancake. PCA would find that you don't really need all three dimensions (x, y, z) to describe it. You could just use two dimensions that describe the "face" of the pancake. It transforms the original, correlated features into a smaller set of new, uncorrelated features called "principal components."
Example Prompt: "We have a dataset with 500 features for each customer. Use PCA to reduce this down to the 10 most important 'principal components' that capture 95% of the variance, making it easier for other models to process."
🧠 III. Deep Learning (Artificial Neural Networks)
This is a more advanced subfield of machine learning that uses complex, multi-layered "neural networks" to solve problems. These models are responsible for the recent boom in AI, including generative AI.
Convolutional Neural Network (CNN): The specialized network for images and grid data.
Deeper Dive: The key invention is the "convolutional filter" (or "kernel"). This is a tiny scanner (e.g., 3x3 pixels) that slides across the entire image. One filter might be trained to detect vertical edges, another to detect corners, and another to detect patches of green. As the data passes through deeper layers, the filters combine these simple features to learn more complex ones, like "eye," "wheel," or "dog snout."
Recurrent Neural Network (RNN): The original network for sequential data (like text or time series).
Deeper Dive: An RNN has a "memory loop." When it processes the word "cat," it produces an output, but it also feeds that output back into itself to use as an input when it processes the next word, "sat." This "hidden state" allows it to remember the context from earlier in the sentence. (Note: Modern models like Transformers have largely replaced RNNs for text).
(New Model) Transformer: The state-of-the-art architecture for language (and now vision). This is the "T" in GPT.
Explanation: The key innovation is "self-attention." Unlike an RNN that reads a sentence one word at a time, a Transformer reads the entire sentence at once. The self-attention mechanism allows each word to "look at" and "pay attention to" all other words in the sentence simultaneously.
Deeper Dive: In the sentence "The animal didn't cross the street because it was too tired," the attention mechanism learns to associate the word "it" most strongly with "animal," not "street." This ability to understand long-range context is what makes models like ChatGPT so powerful and coheren
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