Embeddings in machine learning: what they are, how they work, and when to use them

You have data in a dozen formats, from text and images to user interactions, and your ML models need a way to reason about all of it numerically. An embedding solves this by mapping each input to a learned dense vector in a continuous embedding space, where geometric proximity reflects real-world meaning.

According to a2026 Gartner forecast on vector search and generative AI, more than 30% of new enterprise applications using generative AI will be supported by vector databases in 2026, up from less than 5% in 2023. That makes this a core infrastructure decision rather than an academic exercise.

This guide covers the embedding definition researchers use, what embedding meaning looks like in production systems, how models learn to produce representations, how to choose the right model for your task, and how to monitor quality once your system ships.

What is an embedding?

An embedding is a learned numerical representation of an object, expressed as a vector of real numbers, that captures the object’s semantic meaning in a continuous space. Each dimension encodes a feature your model discovered during the training process. Its position relative to every other object determines how you can compare the two.

From raw data to vectors

Your raw data, whether it is text, pixels, or categorical data, starts in a form that machine learning algorithms cannot process directly. One-hot encoding is the simplest conversion: each category gets its own binary column. A vocabulary of 50,000 words produces 50,000-dimensional sparse vectors with a single 1 and the rest zeros.

That approach wastes memory and encodes no relationships between words. Word embeddings replace that sparse vector with a compact, learned alternative, typically between 128 and 1,536 dimensions.

The values are not hand-assigned. They are learned during training so that objects with similar meanings cluster together. The result is a lower-dimensional space that preserves semantic similarity while remaining efficient for computation.

Categorical data like user IDs, product SKUs, or geographic regions benefits from the same approach. Instead of treating each category as an isolated binary flag, an embedding layer learns representations that capture relationships between categories, so your model can generalize across inputs it has never seen during training.

What embedding means in the context of large language models

Your LLMs rely on representations at every layer. When a token enters a transformer, a lookup layer maps it to a learned vector before any attention computation begins.

These token-level representations carry contextual meaning, because the same word receives a different output depending on the words around it. This is the difference between static embeddings like Word2Vec and contextualized ones from models like BERT and GPT.

Your LLMs also produce output representations you can extract at the sentence level for downstream tasks like clustering, classification, or similarity search.

Why embeddings matter

Your models perform better, train faster, and scale further when they operate on learned representations rather than raw inputs. Each learned representation positions an object relative to every other object in the space, and that structure is what makes downstream tasks possible. The subsections below cover the three main reasons.

Reducing data dimensionality

You often work with high-dimensional data. An image might have millions of pixel values. A document might contain tens of thousands of unique tokens. Feeding that raw dimensionality into a model is computationally expensive and introduces noise.

Dimensionality reduction through embeddings compresses these inputs into a lower-dimensional space, preserving the structure that matters while discarding redundancy. Your ML models train faster, use less memory, and generalize better when they operate on compact representations rather than raw inputs.

Capturing semantic similarity

You need your system to know that “cancel my subscription” and “stop my plan” mean roughly the same thing. Representations encode semantic relationships so that similar meanings map to nearby points.

Cosine similarity between two representations gives you a fast, quantitative measure of how related two objects are. That metric drives search, deduplication, and denoising in production NLP systems.

Enabling language models and downstream tasks

Your downstream tasks, from sentiment analysis to machine translation to recommendation systems, all benefit from pre-trained representations. Instead of learning from scratch, your LLM starts with outputs that already encode deep linguistic or visual structure. Adapting on your domain-specific data then tunes these to your particular task, dramatically reducing the data and compute you need.

What objects can be embedded?

Your pipeline is not limited to text. Practitioners asking what are embeddings in AI usually start with words and documents, but the same approach covers images, audio, and graphs. Here are embedding examples across those modalities.

Words and text

You can represent individual words using models like Word2Vec and GloVe, which learn from co-occurrence patterns in large corpora. Text embeddings extend this to sentences and documents, using models like BERT, Doc2Vec, or Universal Sentence Encoder. These text embeddings capture semantic content at whatever granularity your task requires.

Images

Your image data can be processed using convolutional neural networks like ResNet or VGG, which extract visual features and compress them into fixed-length outputs. More recent models like CLIP learn joint representations for images and text, enabling cross-modal retrieval where a text query returns visually relevant results.

Audio and speech

Your audio data, whether music, speech, or environmental sound, can be converted into spectrograms and then processed with models like VGGish or Wav2vec. The resulting representations capture tonal features, frequency patterns, and temporal structure that support tasks like speech recognition and audio classification.

Graphs and structured data

Your relational data (social networks, knowledge graphs, transaction histories) can also be represented this way. Graph embeddings capture nodes and edges as fixed-length outputs, preserving the structural relationships between entities. These support node classification, link prediction, community detection, and anomaly detection in complex networks.

How embeddings work

Your pipeline follows a consistent pattern regardless of data type, from raw input to a queryable space where each embedding can be compared by similarity.

Input data processing

Your model first tokenizes or segments the raw input. Text gets split into tokens, subwords, or characters. Images are resized and normalized into pixel arrays. Audio is transformed into spectrograms or waveforms. This preprocessing step ensures the data conforms to the shape the model expects.

Feature extraction and dimensionality reduction

Your model then identifies the features that matter. For text, this means learning which word co-occurrence patterns carry semantic weight. For images, it means detecting edges, textures, and shapes.

High-dimensional data, like an image with millions of pixels, gets compressed into a representation of a few hundred dimensions. This compact output retains the essential information while discarding noise. The resulting feature vectors serve as the input to downstream ML models.

Learning through training

Your learned representations are not static lookup values. They are parameters adjusted through backpropagation during the training process. Supervised approaches use labeled data to push similar items together and dissimilar items apart. Unsupervised approaches, like skip-gram or contrastive learning, learn structure from co-occurrence patterns and data augmentation. The training objective shapes what the output captures.

Output and similarity search

Your trained model outputs a fixed-length vector for each input. You compare these using cosine similarity, Euclidean distance, or dot product. The resulting representation carries the semantic content of its input, so objects with similar outputs cluster together. Nearest-neighbor search over them drives applications like semantic search, recommendation, and anomaly detection.

Types of models for producing embeddings

Your choice of model determines what kind of semantic information your outputs encode and how well they generalize across tasks. The table below compares the major families.

Static word representations

You can start with the simplest approach. Word2Vec, released by Google in 2013, introduced two methods: skip-gram, which predicts context from a target word, and CBOW, which predicts a target from context. GloVe takes a different route, factorizing a global word co-occurrence matrix.

Both produce static embeddings, meaning a word always gets the same output regardless of context. These models are fast to train and effective for word-level tasks, but they cannot distinguish between “bank” as a riverbank and “bank” as a financial institution.

Contextualized representations

You get richer outputs from contextualized models. BERT generates representations that depend on the surrounding sentence, so polysemous words receive different outputs in different contexts. GPT models produce similar context-sensitive results. ELMo, an earlier approach, achieved partial context sensitivity using bidirectional LSTMs. These models are more compute-intensive, but they capture nuance that static embeddings miss.

Sentence and document models

You sometimes need a single output for an entire passage. Models like Doc2Vec, Universal Sentence Encoder, and Sentence-BERT produce document embeddings or sentence-level representations optimized for comparison tasks. These are especially useful for semantic search, where you compare a query against a corpus to find the closest matches.

Image and multimodal models

Your computer vision tasks typically use CNN-based architectures like ResNet for image embeddings. Multimodal models like CLIP align image and text outputs in a shared space, so you can search for images using natural language queries. These cross-modal outputs enable applications that span data types, from visual question answering to image-text retrieval.

How models are trained to produce embeddings

Your model’s quality depends entirely on the data it trains on and the objective it optimizes for. The training process follows a consistent sequence across data types.

1. Collect and prepare data: You start with a large, representative dataset. For word-level models, that means a text corpus of billions of tokens, such as Wikipedia or Common Crawl. For image models, you need labeled datasets like ImageNet. Preprocessing normalizes the data: text is tokenized, images are resized, and audio is converted into spectrograms.

2. Choose a training objective: Your training objective tells the model what structure to learn. Skip-gram trains by predicting context words from a target word. Contrastive learning pushes paired items closer together while pushing unrelated items apart. Classification objectives train the model to assign correct labels, with learned representations emerging as a side effect of the hidden layers.

3. Define the neural network architecture: Your architecture determines how the model processes inputs. Shallow networks like Word2Vec use a single hidden layer. Deep learning models like BERT stack multiple transformer layers with attention mechanisms. During training, the model makes predictions, calculates a loss, and adjusts all parameters through backpropagation.

4. Evaluate and refine: You validate your outputs on held-out data using intrinsic benchmarks and downstream task performance, then adjust hyperparameters or training data accordingly.

5. Consider linear alternatives: You do not always need a neural network. PCA reduces dimensionality by projecting data onto its principal components. Singular value decomposition factorizes a matrix into components that preserve the most informative structure. Both are faster and more interpretable than neural approaches, but they cannot learn the nonlinear relationships that deeper models capture. They work well as preprocessing steps or for small, structured datasets.

Building and evaluating embeddings with Mastra

Your RAG pipeline depends on quality at every stage, from chunking documents to retrieving the right context at inference time. Mastra is an open-source TypeScript framework that gives you a built-in RAG pipeline with generation, chunking strategies, and integration with your chosen store.

The framework’s observability dashboard surfaces retrieval quality scores, latency, and chunk relevance across your RAG pipeline.

Mastra supports model routing across 90+ providers through a single interface, so you can switch between OpenAI, Cohere, or open-source models without rewriting your pipeline code. Its evaluation system lets you run retrieval quality evals, scoring whether your chunks return the right context for a given query.

Tracing captures every call with latency, token counts, and inputs, giving you visibility into how your RAG pipeline behaves under real traffic.

Build your first RAG pipeline with Mastra.

How to choose the right model

Your selection depends on your data, your task, your latency budget, and how much labeled data you have. The table below maps common scenarios to recommended model families.

Performance and latency considerations

Your production environment imposes real constraints. Models like Word2Vec and GloVe generate outputs in microseconds. BERT-scale models take milliseconds per input and require GPU resources. If you are indexing millions of documents, throughput matters more than per-item latency. If you are processing user queries in real time, you need a model that returns results within your latency budget.

Dataset size and domain specificity

Your dataset size shapes your approach. Large, general-purpose corpora suit pre-trained models out of the box. Small or highly specialized datasets (medical records, legal filings, proprietary codebases) often require adaptation to produce useful outputs.

Domain-specific terms and relationships may not be well represented in a model trained on Wikipedia. Generalization to your domain depends on how closely your data matches the pre-training corpus.

Pre-trained models vs. custom training

You should default to pre-trained ML models and adapt from there. Training from scratch requires massive datasets, significant compute, and careful hyperparameter tuning. Pre-trained models like BERT, GPT, or ResNet give you a strong starting point. Fine-tuning on your domain data adapts the representations to your specific task with far less data and compute.

Real-world applications of embeddings

Your learned representations become useful the moment you plug them into a downstream system.

Natural language processing and semantic search

You use text embeddings to power semantic search, where a query like “how to cancel my order” retrieves relevant documents even when they use different words. Natural language processing tasks like sentiment analysis, machine translation, question answering, and text classification all depend on learned representations to capture semantic meaning across varied language.

Computer vision

You use image embeddings for classification, object detection, facial recognition, and image retrieval. CNNs like ResNet extract feature vectors that encode visual structure. Cross-modal models like CLIP let you search image databases using natural language queries.

Recommender systems

You represent users and items as fixed-length outputs. The dot product between a user’s representation and an item’s representation predicts preference. Collaborative filtering models learn these from interaction histories. The result is personalized recommendations that improve with every new data point.

Anomaly detection

You use graph embeddings and transaction representations to spot outliers. Fraud detection systems project transactions into a representational space and flag those with unusual distances from the expected distribution. Network anomaly detection uses graph-level outputs to identify nodes with irregular connectivity patterns.

Cross-modal applications

You bridge modalities with joint representations. An image and its caption share a common space, enabling tasks like image captioning, visual question answering, and cross-lingual retrieval. These multimodal outputs connect data types that would otherwise require separate pipelines.

Embeddings in AI agents and retrieval-augmented generation

You encounter representations in AI agent systems whenever your agent needs to connect memory, retrieval, and generation. In the context of agents, they serve as the connective tissue between these three layers.

How agents use embeddings for memory and retrieval

You give your agent long-term memory by processing its past interactions and storing the results in a database optimized for similarity search. When a user asks a question, the agent processes the query, searches the store for semantically similar past interactions or documents, and injects the retrieved context into the prompt.

This retrieval-augmented generation (RAG) pattern lets your model reference information beyond its context window without retraining. Each representation the agent stores becomes a retrievable unit of knowledge that persists across sessions.

Databases and similarity search at runtime

You store your outputs in a database optimized for approximate nearest-neighbor search. At inference time, the agent queries this index with a new input and retrieves the top-k most similar entries.

The choice of similarity metric (cosine, dot product, Euclidean) and index type (HNSW, IVF) affects retrieval speed and accuracy. Your database becomes the agent’s external memory layer.

Drift and consistency across agent runs

Your learned representations are not static once deployed. If you swap or update your model, every stored output becomes incompatible with new query outputs. This drift silently degrades retrieval quality.

You need to re-process your entire corpus whenever you change models, and you need monitoring to detect when distributions shift over time.

Evaluating and monitoring quality in production

Your learned representations need the same rigor as any other model artifact: measure them before deployment, and monitor them after.

Intrinsic evaluation: similarity benchmarks and analogy tasks

Extrinsic evaluation: downstream task performance

You measure quality by how much it improves your actual task. Swap in a new model and track changes in classification accuracy, retrieval precision, or clustering coherence on your production data.

Extrinsic evaluation is the ground truth for whether your outputs work, because a model with perfect analogy scores can still underperform on your specific retrieval task.

Detecting drift and distribution shift

You monitor your output distributions over time. When input data changes (new product categories, unfamiliar terms, or seasonal language shifts) your outputs may no longer represent the data accurately. Statistical tests on distributions, or tracking retrieval quality metrics over time, help you detect drift before it degrades user-facing results.

Tracing retrieval quality in RAG pipelines

You need end-to-end visibility into how your representations perform at retrieval time. Each query produces an output, hits the store, and returns context that feeds the model. If the retrieved context is irrelevant, the generated answer will be wrong regardless of model quality.

Mastra captures traces across this entire pipeline, surfacing latency, retrieval scores, chunk relevance, and final output quality so you can pinpoint exactly where retrieval failures originate.

An agent trace hierarchy showing how calls, retrieval operations, and model invocations appear as individual spans you can inspect for latency and quality.

Wrapping up

You translate raw data into geometric structure whenever you use an embedding in production, from search and recommendations to the memory layers inside your AI agents. Your key decisions are choosing the right model for your data type, evaluating outputs on your actual downstream task, and monitoring for drift once you ship.