AI Glossary

1-shot learning is a form of few-shot learning where AI models learn new tasks with just one example, demonstrating the model's ability to generalize from minimal data.

2-shot learning allows AI models to learn new tasks with exactly two examples, providing slightly more context than 1-shot learning while still being extremely data-efficient.

3-shot learning provides AI models with three examples to learn new tasks, offering a balance between data efficiency and learning effectiveness.

A/B testing in AI systems involves comparing two different model versions, prompts, or configurations to determine which performs better based on specific metrics and user feedback.

Active learning is a machine learning technique where the model identifies the most informative data points for labeling, reducing the amount of labeled data needed for training.

Adversarial training involves training AI models with intentionally crafted examples designed to fool the model, improving robustness and reducing vulnerability to attacks.

Attention mechanisms allow AI models to focus on specific parts of input data, enabling better understanding of context and relationships between different elements.

Batch processing in AI involves processing multiple data samples together, improving computational efficiency and enabling parallel processing on GPUs and TPUs.

Bias in AI refers to systematic errors or prejudices in model predictions, often reflecting historical data patterns or training data imbalances that can lead to unfair outcomes.

Backpropagation is the core algorithm for training neural networks, calculating gradients to update model weights and minimize prediction errors.

Chain-of-thought prompting encourages AI models to show their reasoning process step-by-step, improving accuracy and making outputs more interpretable and trustworthy.

Context length refers to the maximum amount of text or tokens an AI model can process in a single input, affecting how much information the model can consider at once.

Cost optimization in AI involves balancing model performance with computational resources, including strategies for reducing inference time, memory usage, and API costs.

Cross-validation is a technique for assessing AI model performance by training on multiple data subsets, ensuring reliable performance estimates across different data distributions.

Data drift occurs when the statistical properties of input data change over time, potentially degrading AI model performance and requiring retraining or adaptation.

Distillation involves training a smaller, faster model to mimic the behavior of a larger, more complex model, enabling deployment in resource-constrained environments.

Domain adaptation techniques help AI models perform well on new, related domains by leveraging knowledge from source domains while adapting to target domain characteristics.

Embeddings are numerical representations of text, images, or other data that capture semantic meaning, enabling AI models to understand relationships and similarities.

Evaluation metrics measure AI model performance using specific criteria like accuracy, precision, recall, F1-score, or custom business metrics relevant to the use case.

Explainable AI (XAI) refers to techniques that make AI model decisions interpretable and understandable to humans, building trust and enabling better decision-making.

Few-shot learning enables AI models to learn new tasks with minimal examples, leveraging pre-trained knowledge to adapt quickly to new domains or requirements.

Fine-tuning involves adapting pre-trained AI models to specific tasks or domains by training on task-specific data, improving performance while requiring less data than training from scratch.

Feature engineering is the process of creating, selecting, or transforming input variables to improve AI model performance and interpretability.

Ground truth refers to the correct or expected answers used to train and evaluate AI models, typically provided by human experts or authoritative sources.

Gradient descent is the optimization algorithm used to train neural networks, iteratively adjusting model weights to minimize prediction errors.

Hallucination in AI refers to when models generate false or fabricated information that appears plausible but is not based on actual training data or facts.

Hyperparameter tuning involves optimizing AI model configuration settings like learning rates, batch sizes, and architecture choices to achieve optimal performance.

In-context learning allows AI models to learn new tasks by providing examples within the input prompt, enabling adaptation without retraining.

Inference is the process of using a trained AI model to make predictions on new data, typically much faster than training but requiring the model to be already trained.

Iterative refinement involves repeatedly improving AI model outputs through feedback loops, often using human feedback to guide model improvements.

Joint training involves training multiple AI models or components together, allowing them to learn complementary representations and improve overall system performance.

Large Language Models (LLMs) are AI models trained on vast amounts of text data, capable of understanding and generating human-like text across many domains.

Learning rate controls how quickly AI models update their weights during training, affecting convergence speed and final model performance.

Logging in AI systems captures detailed information about model inputs, outputs, decisions, and performance metrics for monitoring, debugging, and improvement.

LLM trace refers to the detailed tracking and recording of all interactions with large language models, including inputs, outputs, prompts, and metadata for debugging and optimization.

Latency in AI systems measures the time between sending a request to a model and receiving a response, critical for user experience and real-time applications.

Model monitoring involves tracking AI model performance, behavior, and health in production, detecting issues like drift, degradation, or unexpected behavior.

Multi-modal AI refers to models that can process and understand multiple types of data (text, images, audio, video) simultaneously.

Model serving involves deploying trained AI models to production environments where they can receive requests and return predictions in real-time.

Neural networks are computational models inspired by biological brains, consisting of interconnected nodes that process information and learn patterns from data.

Natural Language Processing (NLP) is a field of AI focused on enabling computers to understand, interpret, and generate human language.

Observability in AI systems provides visibility into model behavior, performance, and decision-making processes, enabling debugging, optimization, and trust building.

Overfitting occurs when AI models learn training data too well, including noise and irrelevant patterns, leading to poor performance on new, unseen data.

Online learning allows AI models to continuously update and improve as new data becomes available, adapting to changing patterns and requirements.

OpenAI API is a popular platform for accessing large language models, providing developers with tools to integrate AI capabilities into applications.

Output validation ensures AI model responses meet quality standards, including checks for accuracy, relevance, safety, and adherence to guidelines.

Prompt engineering involves designing and optimizing input prompts to get desired outputs from AI models, including techniques like few-shot learning and chain-of-thought.

Product analytics in AI involves tracking user interactions, model performance, and business metrics to understand how AI systems impact user experience and business outcomes.

Pre-training involves training AI models on large, general datasets before fine-tuning on specific tasks, enabling transfer learning and improved performance.

Performance metrics in AI track key indicators like accuracy, response time, throughput, and user satisfaction to measure system effectiveness and identify improvement areas.

Prompt templates are reusable, structured formats for AI model inputs that ensure consistency, reduce errors, and improve output quality across different use cases.

Quantization reduces AI model size and improves inference speed by representing weights and activations with fewer bits, often with minimal accuracy loss.

Quality assurance in AI involves systematic testing, validation, and monitoring to ensure models meet performance, safety, and reliability requirements.

Reinforcement Learning from Human Feedback (RLHF) uses human preferences to train AI models, improving alignment with human values and desired behaviors.

Retrieval-augmented generation (RAG) combines AI language models with external knowledge sources, enabling more accurate and up-to-date responses.

Real-time inference enables AI models to provide immediate responses to user queries, essential for interactive applications and user experience.

Semantic search enables finding information based on meaning rather than exact text matches, using AI embeddings to understand query intent and content relevance.

Supervised learning trains AI models using labeled examples, where the correct output is provided for each input, enabling learning of input-output mappings.

System prompts provide context and instructions to AI models, setting behavior guidelines and ensuring consistent, appropriate responses.

Tokenization breaks text into smaller units (tokens) that AI models can process, affecting model understanding and computational efficiency.

Transfer learning enables AI models to apply knowledge learned from one task to related tasks, reducing training time and improving performance.

Training data consists of examples used to teach AI models, including inputs and expected outputs that guide the learning process.

Training loops are iterative processes where AI models learn from data by repeatedly updating weights, evaluating performance, and adjusting parameters.

Temperature in AI generation controls randomness and creativity in model outputs, with lower values producing more focused responses and higher values enabling more diverse outputs.

Underfitting occurs when AI models are too simple to capture the underlying patterns in data, leading to poor performance on both training and test data.

Unsupervised learning trains AI models to find patterns in data without labeled examples, discovering hidden structures and relationships.

Vector databases store and search AI embeddings efficiently, enabling semantic search, recommendation systems, and similarity-based applications.

Version control for AI models tracks changes, experiments, and deployments, enabling reproducibility and rollback capabilities.

Webhook integration in AI systems enables real-time communication between AI services and external applications, supporting automated workflows and data exchange.

Workflow automation uses AI to streamline business processes, reducing manual tasks and improving efficiency through intelligent decision-making.

Zero-shot learning enables AI models to perform new tasks without specific training examples, relying on general knowledge and understanding.