How AI models learn from data and adapt to specific tasks, including techniques for improving performance, efficiency, and alignment with human preferences.
AI training is the process by which machine learning models learn patterns from data. It’s how a model goes from knowing nothing to being able to perform tasks like understanding language, recognizing images, or making predictions.
Training encompasses several phases:
| Term | Description |
|---|---|
| Adam / AdamW (Adaptive Moment Estimation) | The industry-standard optimization algorithm used to train neural networks, which dynamically adapts the learning rate for every individual parameter. |
| Backpropagation | The fundamental algorithm for training neural networks by calculating and propagating error gradients backward to update weights. |
| Batch Size | The number of training examples processed in a single iteration before the model’s internal parameters are updated. |
| Catastrophic Forgetting | A phenomenon where a model trained on a new task dramatically loses performance on previously learned tasks, essentially “forgetting” earlier knowledge. |
| Chain of Thought | A prompting technique that encourages models to generate intermediate reasoning steps before producing a final answer. |
| Context Engineering | The discipline of designing and optimizing the complete context provided to LLMs to elicit desired behaviors. |
| Convergence | The point during training when the model’s loss function stabilizes and stops decreasing significantly, indicating it has learned the patterns. |
| Data Augmentation | A technique used to artificially increase the size and diversity of a training dataset by applying label-preserving transformations to existing data. |
| Distillation | A training technique where a smaller “student” model learns to mimic a larger “teacher” model, producing a compact model that retains much of the teacher’s performance. |
| DPO | A simplified approach to aligning language models with human preferences that directly optimizes using preference data without requiring a reward model. |
| Federated Learning | A decentralized ML technique where models are trained across multiple devices without exchanging raw data, preserving privacy. |
| Few-Shot Learning | A machine learning approach where models learn to perform tasks from only a handful of examples, rather than requiring thousands of training samples. |
| Fine-tuning | The process of taking a pre-trained AI model and further training it on a specific dataset or task to improve its performance for a particular use case. |
| Gradient Descent | An iterative optimization algorithm that minimizes a loss function by updating model parameters in the direction of steepest descent. |
| Gradient / Gradient Flow | The error signal that propagates backward through a neural network during training, indicating how much each parameter contributed to the mistake. |
| Hyperparameter | A configuration setting external to the model that controls the training process (e.g., learning rate, batch size), set before training begins. |
| In-Context Learning | The ability of LLMs to learn tasks from examples provided within the input prompt, without any parameter updates. |
| Learning Rate | A critical hyperparameter controlling the size of optimization steps during training, with values that are too high causing instability and too low causing slow convergence. |
| LoRA | A parameter-efficient fine-tuning technique that adapts large language models by training only small, low-rank matrices instead of all model parameters. |
| Loss Function | A mathematical function that quantifies the difference between model predictions and ground truth, guiding the training process. |
| Optimizer | An algorithm that updates model parameters based on gradients to minimize loss, driving the learning process during training. |
| Overfitting / Underfitting | Two fundamental failure modes: overfitting (memorizing training data) and underfitting (too simple to capture patterns), representing the bias-variance tradeoff. |
| PEFT | A family of techniques that adapt large models by updating only a small subset of parameters, dramatically reducing fine-tuning costs. |
| Pre-training | The initial phase of training a model on massive datasets to learn broad patterns before specializing through fine-tuning. |
| Prompt | The input text or instruction provided to a language model that guides its response. |
| Prompt Engineering | The practice of designing and refining input prompts to guide LLMs toward desired outputs without modifying model weights. |
| Reinforcement Learning (RL) | A type of ML where an agent learns to make decisions by interacting with an environment, receiving rewards for good actions and penalties for bad ones. |
| Regularization | A set of techniques that prevent a model from overfitting by adding a penalty for complexity, forcing it to learn broader, generalizable patterns. |
| Reward Model | A specialized neural network trained to predict human preferences, serving as the scoring function for reinforcement learning alignment. |
| RLHF | A training technique that aligns AI models with human preferences by using feedback from human raters to guide the model toward generating helpful outputs. |
| Scaling Laws | Empirical relationships describing how AI model performance improves predictably as you increase model size, data, and compute. |
| Self-Supervised Learning | A learning paradigm where models learn from unlabeled data by creating their own supervision signals through pretext tasks. |
| Supervised Learning | A machine learning paradigm where models learn from labeled training data — input-output pairs where the correct answer is provided. |
| Synthetic Data | Artificially generated data designed to mimic real data, used to augment training datasets, address data scarcity, or protect privacy. |
| Training | The process of teaching a machine learning model to recognize patterns by exposing it to data and adjusting its internal parameters. |
| Transfer Learning | A technique where knowledge from one task is applied to a related task, reducing data and compute requirements. |
| Unsupervised Learning | A machine learning paradigm where models discover patterns and structures from unlabeled data without explicit guidance on correct outputs. |
| Validation Set | A subset of data used during training to evaluate the model, tune hyperparameters, and detect overfitting without touching the final test set. |
| Vanishing Gradient Problem | A phenomenon where error signals become exponentially smaller as they backpropagate through deep layers, causing early layers to stop learning. |
| Zero-Shot Learning | A machine learning approach where models perform tasks without seeing any task-specific examples, relying on pre-trained knowledge. |
The training approach has profound implications for:
Understanding training helps organizations make informed decisions about whether to use off-the-shelf models, fine-tune existing ones, or invest in custom training.