Accuracy: The fraction of evaluated examples a model gets right. Accuracy alone can be misleading when one category is much more common than the others.
Agent: An AI system that can choose actions, use tools, and work through steps toward a goal. How independently it operates depends on its design and permissions.
Agentic workflow: A process in which an AI system helps decide which steps or tools to use, rather than following only a fixed sequence.
AGIArtificial general intelligence: A proposed kind of AI able to perform a broad range of intellectual tasks at a human level or beyond. There is no single agreed test for when a system qualifies.
AIArtificial intelligence: Software that performs tasks associated with human intelligence, such as recognizing speech, making predictions, or generating text.
Alignment: The effort to make an AI system behave consistently with intended goals and human expectations. It includes how a system handles instructions, uncertainty, and harmful requests.
APIApplication programming interface: A defined way for software to request data or actions from another service. An AI API lets an application send inputs to a model and receive outputs.
API key: A credential used by software to identify itself to a service. For an AI service, it may also determine which account pays for requests.
Artificial neural network: A machine-learning model built from connected layers of adjustable calculations. Training changes those calculations so the network becomes better at its task.
ASRAutomatic speech recognition: Technology that converts spoken audio into written text. It is used for transcription, captions, and voice interfaces.
Attention: A mechanism that helps a model weigh relationships between parts of its input. In a language model, it helps connect a word or token with relevant surrounding information.
Autoregressive model: A model that predicts the next part of a sequence using the parts already available. Many language models generate text one token at a time this way.

B

Backpropagation: A training method that works backward through a neural network to calculate how its adjustable values contributed to an error.
Base model: A model after its initial training, before additional adaptation for a particular product or task. A base language model may predict text without reliably following chat instructions.
Batch: A group of examples processed together during training, or a group of requests processed together during inference. Batching can improve hardware efficiency.
Batch size: The number of examples or requests processed together. A larger batch often needs more memory and can change training behavior or response timing.
Benchmark: A defined test used to compare AI systems. A benchmark score describes performance on that test, not every task a user might attempt.
BF16Bfloat16: A 16-bit number format often used in AI computation. Its range helps large models train and run with less memory than 32-bit numbers.
Bias: A systematic tendency in data, predictions, or behavior. Depending on context, it can mean a statistical pattern or an unfair difference in how a system treats people.
BPEByte pair encoding: A method often used to build a tokenizer's vocabulary by combining frequently occurring pieces of text. Tokens may be words, word parts, or smaller pieces.

C

Chain of thought: Intermediate reasoning steps used while working toward an answer. Some systems use internal reasoning that is not shown; a visible explanation is not proof of the model's exact internal process.
Chat model: A language model adapted for conversation, usually with messages labeled by role. It may also support tool calls, images, audio, or other inputs.
Checkpoint: A saved state of a model during or after training. It allows training to resume or a particular model version to be used later.
Chunking: Splitting a document into smaller pieces for storage, search, or model input. Chunk size and overlap affect what information can be retrieved.
Classification: Assigning an input to one or more categories. A model might label a message as a billing question, technical issue, or cancellation request.
CLIPContrastive Language–Image Pre-training: A model approach that learns relationships between images and text by comparing matching and nonmatching pairs. It is useful for tasks such as image search.
Clustering: Grouping similar examples without requiring a category label for every example. It can help reveal patterns in a dataset.
CNNConvolutional neural network: A neural network that looks for local patterns using filters that move across an input. CNNs are widely used for image-related tasks.
Coding agent: An AI agent that works with software projects using tools such as file editing, code search, terminals, and tests. Its access and approval settings determine what it can do.
Completion: Output generated by a language model in response to an input. The term comes from systems that continue a supplied piece of text.
Compute: The processing work and hardware capacity needed to train or run a model. More compute can mean more time, more processors, or both.
Computer vision: AI methods for interpreting images and video, such as identifying objects, reading a scene, or measuring movement.
Context: The information available to a model for a particular request. It can include instructions, conversation history, documents, images, and tool results.
Context compression: Shortening or summarizing information to fit more useful material into a model's available context. Compression can lose details that later turn out to matter.
Context engineering: Designing how an AI system receives the information it needs. This includes selecting, organizing, retrieving, and trimming instructions, history, and supporting material.
Context window: The amount of information a model can handle in one request, usually measured in tokens. How inputs and generated output share that limit depends on the model and service.
Contrastive learning: Training a model to bring related examples closer together in its representation and push unrelated examples farther apart.
Cosine similarity: A measure of how closely two vectors point in the same direction. It is commonly used to compare embeddings in semantic search.
Cross-attention: An attention mechanism that connects information from different sequences or representations, such as a text description and an image representation.
CUDACompute Unified Device Architecture: NVIDIA's platform for running general-purpose computation on its GPUs. Many AI libraries use it to accelerate training and inference.

D

Data augmentation: Creating additional training examples by changing existing ones, such as cropping an image. The changes should preserve the information relevant to the task.
Data contamination: Evaluation material appearing in training data or other information available to the system. It can make a reported score look better than performance on genuinely new material.
Data drift: A change in the data a model sees over time. A model trained on older patterns may become less useful when real-world inputs change.
Data labeling: Adding the answers or categories a model is meant to learn from. Labels might identify an object in an image or mark whether a response is helpful.
Dataset: A collection of examples used for training, evaluation, or analysis. A dataset can contain text, images, audio, structured records, or a mixture of formats.
Decoder: A model component that produces an output from a representation or earlier output. Decoder-based language models often generate text token by token.
Decoding: The process of turning a model's predictions into an output sequence. Decoding settings influence which next token is selected.
Deep learning: Machine learning that uses neural networks with many processing layers. It powers many modern language, speech, and image systems.
Dense model: A model that uses most of its parameters for each input, in contrast with architectures that activate selected groups of experts.
Diffusion model: A generative model that learns to create data through a process that reverses added noise. Diffusion-based approaches are widely used for image generation.
Distillation: Training one model to learn from the outputs or behavior of another model. It is often used to make a smaller or faster model approximate a larger one.
DPODirect preference optimization: Adapting a model using examples of preferred and less-preferred responses. It learns from those comparisons without a separate reinforcement-learning loop of the usual RLHF kind.
Dropout: A training technique that temporarily disables selected parts of a neural network to reduce overreliance on particular connections.

E

Embedding: A numerical representation of information, such as a passage or image, designed so useful relationships can be compared mathematically. It is commonly used for semantic search.
Encoder: A model component that converts an input into a representation useful for another task, such as search, classification, or generation.
Encoder–decoder model: An architecture with one component that represents the input and another that generates the output. It is used for tasks such as translation and summarization.
Endpoint: A network address an application calls to use a service. An inference endpoint accepts model inputs and returns the model's results.
Ensemble: A system that combines multiple models or predictions. Combining different approaches can improve reliability, although it adds complexity or cost.
Epoch: One complete pass through a training dataset. Training may involve several epochs, depending on the data and method.
Evaluation: Testing how well an AI system performs for a defined purpose. Useful evaluations check actual tasks and failure cases, rather than relying only on a general benchmark score.
Expert: A specialized component within a mixture-of-experts model. A routing mechanism selects which experts process a particular input.
Explainability: Methods for helping people understand why a model produced a result. An explanation can be an approximation rather than a complete account of internal computation.

F

F1 score: A score combining precision and recall using their harmonic mean. A strong F1 score generally requires both few false alarms and few missed positive cases.
Feature: An input property used by a model, such as a measurement, word pattern, or representation learned from data.
Few-shot prompting: Including a small number of examples in a prompt to demonstrate the desired task or response format. The examples guide the request without retraining the model.
Fine-tuning: Further training an existing model on additional examples to adapt its behavior or task performance. This changes model parameters, unlike simply supplying a longer prompt.
Foundation model: A broadly trained model that can be adapted to many tasks. Language models and some image or multimodal models fall into this category.
FP1616-bit floating point: A number format used to reduce the memory and computation needed for AI workloads. Its precision and numerical range differ from FP32 and BF16.
FP3232-bit floating point: A floating-point number format with more precision and memory use than 16-bit formats. It is also called single precision.
Function calling: A way for a model to request a structured call to an application's function or tool. The application, rather than the model's text alone, decides whether to execute the call.

G

GANGenerative adversarial network: A generative approach that trains one network to produce examples while another learns to distinguish generated examples from real ones.
Generative AI: AI that creates outputs such as text, images, audio, video, or code from patterns learned during training.
GPUGraphics processing unit: A processor that can perform many calculations in parallel. GPUs are widely used to train AI models and run them efficiently.
Gradient: A calculation describing how a change in a model's adjustable values would affect its training error. Training uses gradients to choose updates.
Gradient descent: An optimization method that repeatedly adjusts model parameters in a direction intended to reduce training error.
Grounding: Connecting an AI response to relevant evidence, such as documents, search results, or database records. Grounding can improve factual support but does not guarantee a correct answer.
Ground truth: The reference answers or labels used to train or evaluate a model. These references can still contain mistakes or subjective judgments.
Guardrail: A rule, check, or control used to constrain an AI system's inputs, outputs, or actions. Guardrails can operate before, during, or after a model call.

H

Hallucination: An AI output that presents invented or unsupported information as if it were established fact. A fluent or confident answer can still be a hallucination.
Hidden state: An internal numerical representation maintained or produced by a model while processing information. Its structure depends on the model architecture.
Human in the loop: A process that includes a person in reviewing, approving, correcting, or guiding an AI system's work.
Hybrid search: Search that combines methods such as keyword matching and embedding-based retrieval. It can find both exact names and conceptually related material.
Hyperparameter: A setting chosen to control a training or model process, rather than a value learned directly from examples. Learning rate and batch size are common training hyperparameters.

I

Image-to-image: Generating or editing an image using another image as an input. A text instruction or other controls may also guide the result.
Inference: Using a trained AI model to produce an output from new input. Chat replies, image generation, speech transcription, and classification can all involve inference.
Inference server: Software that loads a model and handles requests to run it. It may manage batching, memory use, streaming, and access to accelerators.
Inpainting: Using an AI model to replace or create content inside a selected part of an image while using the surrounding image as context.
Input token: A token representing information supplied to a model, such as instructions or conversation context. Some services charge different rates for input and output tokens.
Instruction hierarchy: Rules describing which instructions take priority when they conflict. Applications may distinguish system rules, developer instructions, user requests, and untrusted retrieved content.
Instruction tuning: Training a model on examples of instructions and desired responses so it becomes better at following requests.
INT88-bit integer: An integer number format often used when quantizing model values. It can reduce memory use, but quality depends on how quantization is performed.

J

JSONJavaScript Object Notation: A text format for structured data using objects, lists, and values. AI applications often use JSON to exchange tool arguments and structured responses.

K

Knowledge cutoff: The point up to which a model's training data is generally described as current. It does not guarantee knowledge of every earlier fact, and tools can supply newer information.
Knowledge graph: A representation of entities and the relationships between them. It can help a system connect facts across people, places, products, or concepts.
KV cacheKey–value cache: Stored attention information from tokens a model has already processed. Reusing it can speed up text generation, but it uses memory.

L

Label: An expected answer or category attached to a training example. An image might be labeled as containing a bicycle.
Language model: A model trained to represent patterns in language, often by predicting missing or following text. Language models can support tasks beyond producing chat replies.
Latency: The delay between a request and a response. For streaming output, the wait for the first token and the wait for the complete answer are different measurements.
Latent space: An internal representation in which a model expresses features of data as numbers. Generative models can work in this representation before producing a visible or audible result.
Learning rate: A setting that controls the size of parameter updates during training. Updates that are too large or too small can make training ineffective.
LLMLarge language model: A language model with a large number of learned parameters, trained on substantial text data. Many AI assistants use LLMs to understand requests and generate responses.
LLMOpsLarge language model operations: The practices used to run and maintain language-model applications, including prompt versions, model selection, evaluation, monitoring, and cost controls.
Logit: A model's raw score for a possible output, before scores are converted into probabilities. In text generation, logits help determine the next token.
LoRALow-rank adaptation: A fine-tuning technique that trains small added parameter sets while keeping most of the original model fixed. It can make adaptation less expensive than updating every parameter.
Loss: A numerical measure of how far a training result is from the desired result. Training aims to improve this measure, but a lower loss does not automatically mean better real-world behavior.

M

Machine learning: Methods that let software learn patterns from data rather than requiring a person to write a separate rule for every case.
MCPModel Context Protocol: An open protocol through which AI applications can connect to tools, data, and other capabilities supplied by servers. The application controls permissions and how those capabilities are used.
MCP client: The part of an AI application that connects to an MCP server and exchanges protocol messages with it.
MCP server: A program that exposes capabilities through the Model Context Protocol. These can include tools, data resources, or prompt templates.
Memory: Information an AI application preserves for use in later interactions. It may be stored in a database or file; it does not necessarily mean the model's trained parameters changed.
Metadata: Information describing other data, such as an author, date, file type, or topic. Search systems use metadata to organize and filter results.
MLMachine learning: An abbreviation for machine learning: methods that learn patterns from data to make predictions or perform tasks.
MLOpsMachine learning operations: The practices used to deploy, monitor, and maintain machine-learning systems, including model versions, data pipelines, and performance checks.
Model: The learned system that maps inputs to outputs. A deployed AI product may combine a model with tools, interfaces, retrieval, permissions, and other software.
Model card: Documentation describing a model's intended uses, training information, evaluation results, and known limitations. The amount and quality of detail vary by publisher.
Model merging: Combining parameters or adaptations from multiple models to create another model. Success depends on the models and method; it does not simply combine all their abilities.
MoEMixture of experts: An architecture that routes inputs through selected expert components. It can provide a large total parameter count while using only part of the model for each token.
Multi-agent system: A system in which multiple AI agents work together or divide tasks. Coordination adds overhead and does not by itself guarantee a better result.
Multimodal model: A model that works with more than one kind of information, such as text and images or audio and text. Supported input and output types vary by model.

N

NLPNatural language processing: Methods for working with human language in text or speech, including translation, classification, search, and language generation.

O

OCROptical character recognition: Technology that reads text from images or scanned documents and converts it into machine-readable text.
On-device AI: AI inference performed on a user's device rather than entirely on a remote server. Available models and performance depend on the device's hardware and software.
Open source: Software distributed under a license that grants specified rights to inspect, use, modify, and share its source code. A downloadable AI model is not automatically open-source software.
Open weights: Model parameters made available for download. The license determines how they may be used or redistributed; open weights do not necessarily include training code or data.
Optimizer: An algorithm that decides how to update model parameters during training using information such as gradients. Adam and stochastic gradient descent are common examples.
Outpainting: Generating new image content beyond the boundaries of an existing image, using the original image as context.
Output token: A token generated by a model. Depending on the service, reported or billable output can include internal reasoning tokens as well as the visible answer.
Overfitting: When a model learns training examples so closely that it performs poorly on new examples. Strong training results alone do not prove that a model generalizes well.

P

Parameter: A value learned during model training, such as a neural-network weight. Parameter count describes model size, but it is not a complete measure of capability.
PEFTParameter-efficient fine-tuning: Methods that adapt a model by training a relatively small set of parameters. LoRA is one example.
Perplexity: A measure of how well a language model predicts a token sequence. Comparisons need the same data and tokenization; lower perplexity does not guarantee a better assistant.
Pipeline: A series of processing steps that pass information onward. An AI pipeline might extract text, retrieve relevant passages, and then generate an answer.
Precision: For a chosen positive category, the fraction of predicted positive results that are actually correct. It describes how trustworthy those positive detections are.
Prefill: The stage of language-model inference that processes the input context before generating new output tokens.
Pretraining: The initial broad training used to build a model before more specific adaptation. For many language models, it involves learning to predict text from large collections of examples.
Prompt: The input and instructions supplied to guide a model's response. In a chat application, the full prompt can include more than the message typed by the user.
Prompt engineering: Writing and testing instructions, examples, and context to improve a model's results for a particular task.
Prompt injection: An attempt to make an AI system treat untrusted content as instructions that override its intended task or rules. It can arrive through a webpage, document, tool result, or user input.
Prompt template: A reusable instruction structure with places for changing information, such as a customer name, document, or desired output format.
Pruning: Removing or disabling selected model parameters or structures to reduce size or computation. Quality depends on what is removed and how the model is adapted afterward.

Q

QLoRAQuantized low-rank adaptation: A fine-tuning approach that combines a quantized base model with trainable LoRA adapters to reduce the memory needed for adaptation.
Quantization: Representing model values with fewer bits to reduce memory use or computation. Effects on speed and output quality depend on the method, hardware, and model.

R

RAGRetrieval-augmented generation: Retrieving relevant information and supplying it to a model when generating an answer. The material supplements the request without requiring retraining on that information.
Rate limit: A service's limit on how often or how much an application can request within a period. Limits may apply to requests, tokens, concurrent work, or another measure.
Reasoning model: A model designed or trained to spend additional computation working through a problem before answering. The approach and visibility of intermediate reasoning vary by system.
Recall: For a chosen positive category, the fraction of actual positive examples the system finds. It describes how much relevant material or how many true cases were missed.
Red teaming: Deliberately testing an AI system with challenging or adversarial cases to discover weaknesses and harmful failure modes.
Regression: Predicting a numerical value, such as a price or temperature, rather than assigning only a category. In software testing, regression instead means a previously working behavior breaks.
Reinforcement learning: Training an agent or model using feedback about the outcomes of its actions. The goal is to learn behavior that achieves more reward over time.
Reranking: Taking an initial set of search results and using another scoring step to put the most relevant results first.
Retrieval: Finding information relevant to a request. Retrieval can use keywords, embeddings, metadata filters, or combinations of these methods.
RLHFReinforcement learning from human feedback: A family of methods using human preference information to guide behavior through reinforcement learning. Human feedback is often used to train a reward model first.
RNNRecurrent neural network: A neural network that carries a state from one step of a sequence to the next. It has been used for text, time series, and speech.

S

Sampling: Selecting an output from a model's predicted possibilities rather than always choosing the highest-scoring option. Sampling settings influence output variation.
Sandbox: An isolated or restricted environment for running software or tools. Its protection depends on the permissions and boundaries the system enforces.
SDKSoftware development kit: Libraries and tools that help developers use a platform. An AI SDK can simplify authentication, model requests, streaming, and tool integration.
Seed: A value used to initialize a random process. Reusing a seed may improve reproducibility, but identical results also depend on the model, software, settings, and hardware.
Self-attention: An attention mechanism that compares positions within the same sequence, helping a model connect relevant parts of an input with each other.
Self-hosted model: A model run on infrastructure controlled by the operator, rather than accessed only through another provider's hosted model service.
Self-supervised learning: Learning from training targets derived from the data itself, rather than a separate human label for every example. Predicting the next token in text is one example.
Semantic search: Search based on meaning or related concepts, rather than only exact word matches. It often uses embeddings to compare a query with candidate results.
SFTSupervised fine-tuning: Adapting a model with examples of inputs and desired outputs. It is commonly used to teach response styles, formats, or instruction-following behavior.
Skill: A reusable package of instructions, and sometimes supporting files, that an AI application or agent can use for a task. The format and installation process depend on the application.
Small language model: A relatively compact language model that uses fewer parameters or less compute than larger alternatives. There is no universal size boundary for the label.
Speculative decoding: An inference technique that proposes output tokens with a faster draft process and checks them with the target model. It can reduce generation time when the setup is suitable.
Speech-to-text: Converting spoken audio into written text. The output might be a transcript, captions, or text used by another part of an application.
Streaming: Delivering output in pieces as it becomes available instead of waiting for the complete result. The request may still be running while earlier pieces are displayed.
Structured output: A model response constrained or validated against a specified format, such as a JSON schema. A valid format does not by itself guarantee factual correctness.
Supervised learning: Training with examples that include expected answers or labels. The model learns to map inputs to those supplied targets.
Synthetic data: Data produced artificially rather than collected directly from the original real-world process. It can help with training or testing, but may introduce its own errors or biases.
System prompt: An instruction message used by an application to guide a model's behavior. Its priority and treatment depend on the model service and application.

T

Temperature: A setting that changes how sharply a model favors higher-scoring next tokens during sampling. Higher settings usually allow more varied choices; they do not make a model more knowledgeable.
Tensor: A numerical array with one or more dimensions. AI systems use tensors to represent inputs, model parameters, and intermediate calculations.
Test set: Examples held back to evaluate a model after training and model selection. Repeatedly tuning to a test set weakens its value as an independent check.
Text-to-image: Generating an image from a text description. The result depends on the model, prompt, settings, and any additional guidance.
Text-to-speech: Generating spoken audio from written text. Systems may offer different voices, languages, speaking styles, or controls.
Throughput: The work a system completes in a period, such as requests per second or tokens per second. High throughput is different from a short wait for one request.
Token: A unit of input or output used by a model's tokenizer. A token may be a word, part of a word, punctuation, or another encoded piece of information.
Tokenization: Splitting and encoding information into the units a model processes. Different models can tokenize the same text differently.
Tool calling: A model requesting an external capability through structured arguments, such as a search or calculation. The surrounding application decides which tools exist and whether a requested action runs.
Top-k: A sampling setting that limits the next-token candidates to a specified number of the highest-scoring choices.
Top-p: A sampling setting that keeps next-token candidates whose combined probability reaches a chosen threshold. It is also called nucleus sampling.
TPUTensor processing unit: An accelerator developed by Google for machine-learning computation. Its software and deployment options differ from those of a general-purpose GPU.
Training: Adjusting a model using data and a learning objective. Training changes the model, while ordinary inference applies an existing model to an input.
Training data: The examples used to adjust a model during training. A model can learn useful patterns and also inherit gaps, errors, or biases from those examples.
Training split: The portion of a dataset used to fit a model. Separate validation and test splits help assess decisions and performance on examples not used for that fitting.
Transfer learning: Using a model or representation learned for one setting as a starting point for another task or dataset.
Transformer: A neural-network architecture built around attention mechanisms. It is used in many language models and also in systems for images, audio, and other data.
TTFTTime to first token: The time from sending a request until the first output token becomes available. It measures responsiveness for streaming language models.
TTSText-to-speech: An abbreviation for technology that turns written text into spoken audio. Voice and language options depend on the system.

U

Underfitting: When a model has not learned enough of the relevant patterns to perform well, including on its training examples.
Unsupervised learning: Learning patterns from data without an expected answer label for every example. Clustering is one common example.

V

Validation set: Examples used to guide training choices or model selection without directly fitting the model to those examples in the usual training loop. A separate test set supports final evaluation.
Vector: An ordered collection of numbers. In AI search, a vector often represents an embedding that can be compared with other embeddings.
Vector database: A database designed to store vectors and find nearby or similar vectors efficiently. It is often used to retrieve passages or other items by their embeddings.
VLMVision-language model: A model that works with both visual information and language. It might describe images, answer image questions, or support other visual tasks.
VRAMVideo random-access memory: Memory associated with a graphics processor. Model weights, working data, and attention caches can all compete for this memory during inference.

W

Weights: Learned numerical values that influence a neural network's calculations. Downloading weights does not necessarily provide training data or the right to redistribute the model.
Workflow: A sequence of tasks and decisions organized to achieve an outcome. AI may handle individual steps, while others can be ordinary software or human review.

Z

Zero-shot prompting: Asking a model to perform a task without supplying examples of that task in the prompt. The model relies on its training and the current instructions.

DaveAI.

An independent library of AI resources and practical reading. Original creators are credited on their resources.

Resource directory Industry news AI glossary Using Dave AI Contribute Owner access