AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
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Parallel Computing
Processing multiple computations simultaneously rather than sequentially. Parallel computing is fundamental to AI training and inference, which involve massive matrix operations.
Parallel Function Calling
The ability of an LLM to invoke multiple tool calls simultaneously in a single response, rather than sequentially. This enables faster task completion for independent operations.
Parameter
Any learnable value in a machine learning model that is adjusted during training. Parameters include weights and biases in neural networks. Model size is often described by parameter count.
Perceptron
The simplest form of a neural network — a single neuron that takes weighted inputs, sums them, and applies an activation function to produce an output. It is the fundamental building block of neural networks.
Perplexity
A metric that measures how well a language model predicts text. Lower perplexity indicates the model is less 'surprised' by the text, meaning it can predict the next token more accurately.
Pinecone
A managed vector database service designed for AI applications. Pinecone handles the infrastructure complexity of storing, indexing, and querying high-dimensional vectors at scale.
Planning
An AI agent's ability to break down complex goals into a sequence of steps and determine the best order of actions to accomplish a task. Planning involves reasoning about dependencies, priorities, and contingencies.
Playground
An interactive web interface where users can experiment with AI models by adjusting parameters, testing prompts, and seeing results in real time without writing code.
Positional Encoding
A technique used in transformers to inject information about the position of each token in a sequence. Since transformers process all tokens in parallel, they need explicit position information.
Pre-training
The initial phase of training a model on a large, general-purpose dataset before specializing it for specific tasks. Pre-training gives the model broad knowledge and capabilities.
Precision
Of all the items the model predicted as positive, the proportion that were actually positive. Precision measures how trustworthy the model's positive predictions are.
Preference Optimization
Training techniques that directly optimize models based on human preference data, where humans indicate which of two model outputs they prefer.
Principal Component Analysis
A dimensionality reduction technique that transforms data into a new coordinate system where the first axis captures the most variance, the second axis the next most, and so on.
Privacy-Preserving ML
Machine learning techniques that train models or make predictions while protecting the privacy of individual data points. Includes federated learning, differential privacy, and homomorphic encryption.
Prompt Attack Surface
The total set of potential vulnerabilities in an LLM application that can be exploited through prompt-based attacks, including injection, leaking, and jailbreaking vectors.
Prompt Caching
A technique that stores and reuses the processed form of frequently used prompt prefixes, avoiding redundant computation. It speeds up inference and reduces costs for repeated prompts.
Prompt Chaining
A technique where the output of one LLM call becomes the input for the next, creating a pipeline of prompts that together accomplish a complex task.
Prompt Compression
Techniques for reducing the token count of prompts while preserving their essential meaning, enabling more efficient use of context windows and reducing API costs.
Prompt Engineering
The practice of designing and optimizing input prompts to get the best possible output from AI models. It involves crafting instructions, providing examples, and structuring queries to guide the model toward desired responses.
Prompt Injection
A security vulnerability where malicious input is crafted to override or manipulate an LLM's system prompt or instructions, causing it to behave in unintended ways.
Prompt Injection Defense
Techniques and strategies for protecting LLM applications from prompt injection attacks, including input sanitization, output filtering, and architectural defenses.
Prompt Leaking
When a user successfully extracts a system's hidden system prompt through clever questioning. Prompt leaking reveals proprietary instructions, business logic, and safety configurations.
Prompt Library
A curated collection of tested, optimized prompts organized by use case. Prompt libraries accelerate development by providing proven starting points for common tasks.
Prompt Management
The practice of versioning, testing, and managing prompts used in LLM applications. It treats prompts as code that needs proper lifecycle management.
Prompt Optimization
Systematic techniques for improving prompt effectiveness, including automated prompt search, A/B testing of prompt variants, and iterative refinement based on output quality metrics.
Prompt Template
A pre-defined structure for formatting prompts to AI models, with placeholders for dynamic content. Templates ensure consistent, optimized prompt formatting across applications.
Prompt Tuning
A parameter-efficient fine-tuning technique that prepends learnable 'soft prompt' tokens to the input while keeping the main model weights frozen. Only the soft prompt parameters are trained.
Prompt Versioning
Tracking different versions of prompts over time, including changes, performance metrics, and rollback capabilities. Essential for managing prompts in production AI applications.
Pruning
A model compression technique that removes unnecessary or redundant weights, neurons, or layers from a trained neural network. Like pruning a plant, it removes parts that are not contributing to overall health.