> ## Documentation Index > Fetch the complete documentation index at: https://developers.telnyx.com/llms.txt > Use this file to discover all available pages before exploring further. # AI glossary > Glossary of common artificial intelligence and machine learning terms including alignment, inference, RAG, latency, and prompt engineering. | Term | Definition | | :----------------------------------- | :-------------------------------------------------------------------------------------- | | AI Alignment | Ensuring AI behavior aligns with human goals and values. | | API Latency | Time delay between request and response in a deployed AI service. | | AUC (Area Under Curve) | Measure of model’s ability to distinguish between classes. | | Accuracy | Proportion of correct predictions made by a model. | | Activation Function | Function that introduces non-linearity into a neural network (e.g., ReLU, Sigmoid). | | Artificial Intelligence (AI) | Simulation of human intelligence in machines programmed to think and learn. | | Attention Mechanism | Mechanism that enables models to focus on relevant parts of the input. | | Backpropagation | Algorithm for updating neural network weights. | | Batch Normalization | Normalizes activations to improve training speed and stability. | | Bias | Systematic error introduced by assumptions in data or model. | | Chain-of-Thought (CoT) | Technique where models generate intermediate reasoning steps. | | Computer Vision | AI field that enables machines to interpret and make decisions based on visual data. | | Confusion Matrix | Table showing true vs predicted classifications. | | Data Privacy | Protection of sensitive user data during model training and usage. | | Deep Learning | Subset of ML using neural networks with many layers to model complex patterns. | | Dropout | Regularization technique that randomly drops units in a neural network during training. | | Embedding | Numerical representation of data, often used for similarity or search. | | Epoch | One complete pass through the training dataset. | | Explainable AI (XAI) | Techniques to interpret and understand model predictions. | | F1 Score | Harmonic mean of precision and recall. | | Few-shot Learning | Model learns from a few labeled examples. | | Fine-Tuning | Training a pre-trained model on a specific task or dataset. | | Generative AI | AI models that can generate new content like text, images, audio, or code. | | Gradient Descent | Optimization algorithm for minimizing loss function. | | Hallucination | Generated output that is fluent but factually incorrect. | | HuggingFace | Ecosystem for pretrained NLP models and transformers. | | Inference | Running a trained model to make predictions. | | LangChain | Framework for building LLM-powered applications using composable chains. | | Large Language Model (LLM) | A transformer-based model trained on large corpora of text data. | | LlamaIndex | Tool for indexing and querying external data with LLMs. | | LoRA (Low-Rank Adaptation) | Efficient method for fine-tuning large models with fewer parameters. | | Loss Function | Function that measures error between predicted and actual values. | | Machine Learning (ML) | Subset of AI that allows systems to learn from data without explicit programming. | | Mixture of Experts | Model architecture that routes input through subsets of expert networks. | | Model Checkpointing | Saving model states during training to resume or analyze progress. | | Model Fairness | Ensuring model performance does not discriminate against subgroups. | | Model Serving | Hosting and providing access to ML models for inference. | | Multimodal AI | Models that process and generate across multiple data types (e.g., text, image, audio). | | Natural Language Processing (NLP) | AI branch that deals with understanding and generation of human language. | | Neural Network | Computational model inspired by the human brain, used in deep learning. | | ONNX | Open Neural Network Exchange; format for model interoperability. | | Open Weight Model | AI model with publicly available weights for reuse and fine-tuning. | | Overfitting | Model learns training data too well and fails to generalize. | | Positional Encoding | Adds information about the position of tokens in sequences to transformer models. | | Pre-training | Initial training of a model on a large generic dataset. | | Precision | Proportion of true positive predictions among all positive predictions. | | Prompt Engineering | Crafting effective prompts to guide LLM responses. | | Proprietary Model | Model with restricted access, typically hosted and maintained by a company. | | PyTorch | Popular deep learning library developed by Facebook. | | ROC Curve | Graphical plot showing performance of classification model. | | Recall | Proportion of true positives among all actual positives. | | Reinforcement Learning | Learning method where agents learn by taking actions and receiving rewards. | | Residual Connection | Skip connections in neural networks that help prevent vanishing gradients. | | Responsible AI | Ethical and accountable development and deployment of AI. | | Retrieval-Augmented Generation (RAG) | LLM approach that augments prompts with relevant context from a document store. | | Scikit-learn | Python library for traditional machine learning. | | Self-Attention | Mechanism allowing models to weigh importance of different parts of input. | | Semi-Supervised Learning | Uses a small amount of labeled data with a large amount of unlabeled data. | | Supervised Learning | Training a model on labeled data. | | TensorFlow | Google’s open-source deep learning framework. | | TensorRT | NVIDIA platform for optimizing and deploying deep learning models. | | Tokenization | Breaking text into smaller units (tokens) for processing. | | Transformer | Deep learning model architecture that uses self-attention for sequence tasks. | | Underfitting | Model is too simple to capture underlying patterns in data. | | Unsupervised Learning | Training a model on data without labels to find patterns. | | Variance | Model's sensitivity to small fluctuations in training data. | | Vector Database | Database optimized for storing and querying high-dimensional embeddings. | | Zero-shot Learning | Model predicts on tasks without having seen labeled examples. |