What Are Embeddings? A Developer-Friendly Guide to Embeddings in AI
Embeddings are numerical representations of data that capture meaning, relationships, and patterns in a form that machine learning systems can compare.
Introduction
Embeddings are numerical representations of data that capture useful relationships between pieces of information. In AI applications, embeddings allow computers to compare text, images, and other data based on meaning rather than relying only on exact matches.
Embeddings are one of the most important building blocks behind modern AI search and Retrieval-Augmented Generation (RAG).
When you ask a RAG application:
"How can I install Java?"
the system needs a way to find information that means something similar, such as:
"Steps to set up the Java Development Kit on Windows."
The wording is different, but the meaning is closely related.
Embeddings make this kind of semantic comparison possible.
In the previous article about RAG, we looked at how RAG retrieves relevant information before an LLM generates an answer. Now we're going one level deeper and looking at one of the technologies that makes that retrieval possible.
What Are Embeddings ?
Quick Answer
An embedding is a numerical representation of information that captures meaningful relationships between data points.
For text, an embedding model converts words, sentences, paragraphs, or documents into a vector of numbers.
For example:
"How do I install Java?"
↓
Embedding Model
↓
[0.12, -0.41, 0.73, 0.08, ...]
The actual vector will usually contain many more values.
You don't need to understand what each individual number represents.
The important part is that the entire vector provides a mathematical representation that can be compared with other vectors.
That gives an AI application a way to determine whether two pieces of information are related.
Why Do We Need Embeddings?
Computers are very good at comparing exact values.
Human language isn't that simple.
Consider these questions:
How do I install Java?
How can I set up the Java JDK?
What are the steps to get Java running?
A human can immediately understand that all three questions are asking about roughly the same thing.
A basic keyword search might struggle because the words aren't identical.
This is where embeddings become useful.
Instead of asking:
"Do these two texts contain the same words?"
we can ask:
"How semantically similar are these two pieces of text?"
That difference is important for AI search, RAG, recommendation systems, and many other applications.
How Do Embeddings Work?
Embeddings are generated by an embedding model.
You provide the model with some information, such as text.
The model processes it and produces a vector.
Text
↓
Embedding Model
↓
Vector
For example:
"What is RAG?"
↓
Embedding Model
↓
[0.18, -0.42, 0.71, 0.09, ...]
Now imagine another sentence:
"How does Retrieval-Augmented Generation work?"
It will also produce a vector.
If the embedding model has learned that these sentences are semantically related, their vectors should reflect that relationship.
The actual numbers aren't meaningful to a human by themselves.
Their value comes from how the vectors relate to other vectors.
What Is a Vector ?
Before going deeper, let's clarify the word vector.
A vector is simply an ordered collection of numbers.
For example:
[0.12, -0.41, 0.73, 0.08]
This vector has four dimensions.
An embedding might look more like:
[0.12, -0.41, 0.73, 0.08, 0.31, -0.17, ...]
with hundreds or thousands of values.
When an embedding model converts text into a vector, the resulting vector becomes a mathematical representation of that text.
That representation can then be stored, compared, searched, clustered, or used by other machine learning systems.
How Text Becomes an Embedding
Let's use a simple example.
Suppose we have this sentence:
Java is a programming language.
An embedding model processes the sentence and produces something conceptually similar to:
[0.21, -0.13, 0.77, 0.05, ...]
Now consider:
Java is commonly used for software development.
It may produce another vector:
[0.23, -0.10, 0.74, 0.07, ...]
These values are only examples.
A real embedding model will produce its own vector with a specific number of dimensions.
The important idea is that the two vectors can be mathematically compared.
If the model considers the two sentences related, their vectors should occupy similar regions of the embedding space.
What Is Semantic Similarity?
Semantic similarity means similarity in meaning.
This is where embeddings become particularly useful.
Consider:
"How do I install Java?"
and:
"How can I set up the Java Development Kit?"
The wording is different.
The intent is similar.
An embedding-based system can represent both pieces of text as vectors and calculate how closely they relate.
Now compare that with:
"How do I install Java?"
and:
"What is the capital of France?"
These questions have very different meanings.
Their embeddings should therefore be less similar.
You can visualize the basic idea like this:
Java
●
/ \
/ \
JDK ● ● Programming
Cooking
●
Real embedding spaces aren't two-dimensional.
They can contain hundreds or thousands of dimensions.
The diagram is simply a mental model.
Think of embeddings as placing information on a giant mathematical map where related concepts tend to occupy nearby regions.
How Is Similarity Calculated?
Once information has been converted into vectors, an application needs a mathematical method to compare those vectors.
One commonly used method is cosine similarity.
Cosine similarity measures the angle between two vectors.
Conceptually:
Vector A
↗
/
/
●──────────────→ Vector B
If the vectors point in similar directions, the similarity score is generally higher.
A simplified formula is:
cosine similarity =
(A · B) / (||A|| × ||B||)
You usually won't calculate this manually when building a RAG application.
Vector search systems and databases handle the underlying calculations.
What matters is understanding why similarity calculations exist.
The system needs a way to determine which stored information is most relevant to the user's query.
Embeddings in RAG
Now let's connect embeddings to the RAG architecture from the previous article.
A simplified RAG pipeline looks like this:
Documents
↓
Chunking
↓
Embeddings
↓
Vector Database
↓
Retrieval
↓
LLM
↓
Answer
Embeddings sit directly inside the retrieval process.
Suppose you have a documentation website with thousands of pages.
The system can divide the content into smaller chunks:
Chunk 1:
How to install Java
Chunk 2:
Java garbage collection
Chunk 3:
Spring Boot configuration
Chunk 4:
Docker container basics
Each chunk can then be converted into an embedding.
Chunk 1 → Vector 1
Chunk 2 → Vector 2
Chunk 3 → Vector 3
Chunk 4 → Vector 4
Those vectors can then be stored in a vector database.
When a user asks a question, the system creates an embedding for that question and searches for similar vectors.
That's how the retrieval stage works.
Document Embeddings
During the indexing stage, your application converts document chunks into embeddings.
The process looks like this:
Document
↓
Chunk
↓
Embedding Model
↓
Embedding Vector
↓
Vector Database
The application will often store the original text along with the vector.
It may also store metadata.
For example:
{
"text": "The JDK is required to develop Java applications.",
"source": "java-guide.md",
"category": "java",
"version": "2026"
}
This creates a useful combination.
The vector helps with semantic retrieval.
The original text provides the content that can later be passed to the LLM.
The metadata can help with filtering.
For example, you might want to retrieve only documentation for:
version = 2026
instead of returning older documentation.
Query Embeddings
Now let's look at what happens when a user asks a question.
Suppose the user asks:
How can I install Java on Windows?
The application converts the question into an embedding.
User Question
↓
Embedding Model
↓
Query Vector
The query vector can then be compared against vectors stored in the database.
Query Vector
↓
Similarity Search
↓
Stored Document Vectors
↓
Most Relevant Chunks
The retrieved chunks can then be passed to the language model.
The complete flow becomes:
Question
↓
Query Embedding
↓
Vector Search
↓
Relevant Chunks
↓
Context
↓
LLM
↓
Answer
This is one of the most important flows to understand before building a RAG application.
Why Do We Embed Both Documents and Questions?
This is a common beginner question.
Why not embed the documents and leave the user's question as normal text?
Because the retrieval system needs a common representation for comparison.
Imagine your knowledge base contains 100,000 document chunks.
When a user asks a question, the system needs to determine which of those chunks are relevant.
It can do this by representing the question as a vector.
DOCUMENTS
Document Chunk
↓
Embedding Model
↓
Vector
↓
Vector Database
QUERY
User Question
↓
Embedding Model
↓
Query Vector
↓
Similarity Search
↓
Relevant Chunks
The document embeddings and query embedding need to be compatible with the retrieval setup.
That allows the system to compare them.
A Simple RAG Retrieval Example
Imagine our knowledge base contains four chunks:
Chunk A:
Java is a programming language used for building applications.
Chunk B:
The Java Development Kit is required to develop Java applications.
Chunk C:
The Java Virtual Machine executes Java bytecode.
Chunk D:
Docker packages applications into containers.
The user asks:
What do I need to build Java applications?
The question is converted into a query embedding.
The retrieval system compares it with the stored vectors.
Conceptually, the results might look like:
Chunk B ████████████████████
Chunk A ███████████████
Chunk C █████████
Chunk D ██
The system can then retrieve the most relevant chunks.
Those chunks become context for the LLM.
The LLM uses that context to generate the final response.
Notice what's happening.
The system isn't simply looking for an exact phrase.
It's trying to find information that is meaningfully related to the question.
Embeddings and Vector Databases
Embeddings and vector databases are closely connected, but they're different things.
An embedding model creates the vectors.
A vector database stores and searches the vectors.
Think of the relationship like this:
Embedding Model
↓
Creates Vectors
↓
Vector Database
↓
Stores Vectors
↓
Similarity Search
↓
Relevant Results
Popular technologies used for vector search include:
PostgreSQL with pgvector
Qdrant
Milvus
Weaviate
Pinecone
We'll explore these technologies in the upcoming Vector Databases Explained article.
For now, remember the distinction:
Embedding model = creates the representation
Vector database = stores and searches the representation
What Is Embedding Dimension?
You'll often see the term embedding dimension.
It simply means the number of values contained in an embedding vector.
For example:
[0.12, -0.41, 0.73]
has three dimensions.
A real embedding might look like:
[0.12, -0.41, 0.73, 0.08, 0.31, ...]
and contain hundreds or thousands of values.
Different embedding models can produce different dimensions.
This matters when configuring your vector database or search index.
If your embedding model produces vectors with a particular dimensionality, your vector storage system needs to support that same representation.
Are More Dimensions Better?
Not necessarily.
It's easy to assume:
More dimensions
↓
Better embeddings
But that's not how it works.
Embedding quality depends on many factors, including:
The embedding model
Training data
Your domain
Language
Search task
Document quality
Query type
Evaluation methodology
A larger vector isn't automatically more useful.
For a production application, you should evaluate an embedding model against your own data and queries.
That's far more useful than choosing a model simply because it produces a larger vector.
Embedding Models
An embedding model is responsible for converting information into vectors.
Different models can have different characteristics.
They may vary in:
Vector dimensions
Retrieval performance
Supported languages
Latency
Cost
Hardware requirements
Context limits
Domain performance
Some embedding models are accessed through APIs.
Others can be run locally.
The right choice depends on your application.
If you're building a small prototype, simplicity and cost may matter most.
If you're building a large production search system, retrieval quality, latency, infrastructure requirements, and operating cost become much more important.
Don't spend hours comparing models before you understand what you're actually trying to retrieve.
First build a small evaluation set.
Then compare models against it.
Embeddings vs LLMs
Embedding models and large language models serve different purposes.
An LLM is generally designed for understanding and generating language.
An embedding model is designed to transform information into numerical representations that can be compared and searched.
A RAG application may use both:
RAG APPLICATION
│
┌───────────┴───────────┐
↓ ↓
Embedding Model LLM
↓ ↓
Vectors Answer
↓
Retrieval
The embedding model helps the application find relevant information.
The LLM uses the retrieved information to generate the response.
That's why replacing one with the other isn't usually the right way to think about them.
They solve different problems.
Embeddings vs Keyword Search
Let's say a user searches:
How can I install Java?
A traditional keyword search system might focus on words such as:
install
Java
An embedding-based system can compare the meaning of the query against the meaning of stored content.
That means content such as:
Steps to set up the Java Development Kit
can potentially be retrieved even though it doesn't use exactly the same wording.
But this doesn't mean keyword search is obsolete.
Far from it.
Suppose a developer searches for:
NullPointerException
or:
HTTP 404
Exact matching can be extremely useful.
That's why modern retrieval systems often combine semantic retrieval with keyword-based retrieval.
This approach is commonly known as hybrid search.
We'll cover it later in the series.
Embeddings vs Semantic Search
These terms are related, but they're not identical.
Embeddings are the numerical representations.
Semantic search is a search technique that uses meaning to find relevant information.
A simplified semantic search system can work like this:
User Query
↓
Embedding Model
↓
Query Vector
↓
Vector Search
↓
Relevant Documents
So embeddings can be one of the technologies used to implement semantic search.
Think of it this way:
Embedding = representation
Semantic search = retrieval approach
That distinction becomes useful when you start learning how different search systems are designed.
Where Are Embeddings Used?
RAG is one of the most important use cases, but embeddings are much broader than RAG.
Semantic Search
Search for information based on meaning rather than exact words.
Recommendation Systems
Find products, articles, videos, or other content that are similar to something a user has already viewed.
Document Clustering
Group documents that discuss similar topics.
Duplicate Detection
Find content that is semantically similar even when the wording differs.
Classification
Use embeddings as representations for downstream machine learning tasks.
Image Search
Images can also be represented as vectors, allowing systems to find visually or semantically related content depending on the model.
Multimodal AI
Some models can represent different types of data in compatible vector spaces.
This makes embeddings useful far beyond text search.
Embeddings in a RAG Application
Let's put everything together.
Suppose your application contains thousands of documents.
The indexing process might look like:
INDEXING
Documents
↓
Text Extraction
↓
Chunking
↓
Embedding Model
↓
Vectors
↓
Vector Database
When the user asks a question:
QUERY
User Question
↓
Embedding Model
↓
Query Vector
↓
Similarity Search
↓
Relevant Chunks
↓
Context
↓
LLM
↓
Answer
This is the connection between embeddings and RAG.
The embedding model doesn't generate the final answer.
It helps the application find the information that the LLM needs.
What Happens When Retrieval Fails?
This is where things get interesting.
Suppose the user asks:
How do I configure authentication in version 5?
But your retrieval system returns information about version 3.
The LLM now has bad context.
User Question
↓
Query Embedding
↓
Poor Retrieval
↓
Wrong Context
↓
LLM
↓
Potentially Wrong Answer
The LLM can't reliably use information it never receives.
This is why a production RAG system can't focus only on the language model.
You also need to think about:
Chunking
Embedding quality
Metadata
Retrieval algorithms
Filtering
Reranking
Evaluation
A good LLM doesn't automatically fix poor retrieval.
Common Embedding Mistakes
Using Incompatible Embedding Setups
Documents and queries need to be represented in a compatible way for the retrieval system you're using.
Changing your embedding model can require you to regenerate your stored document embeddings.
Ignoring Your Domain
An embedding model that performs well on general text may not perform equally well on highly specialized technical content.
Always test it with real examples.
Assuming Bigger Vectors Are Better
Vector size alone doesn't tell you how useful an embedding model will be.
Evaluate actual retrieval performance.
Changing Models Without Reindexing
If you switch embedding models, your existing vectors may no longer be compatible with the new setup.
You may need to re-embed your documents.
Ignoring Chunking
Even a good embedding model can't retrieve useful information if the underlying chunks are poorly designed.
Embedding quality and chunking quality work together.
Skipping Evaluation
A search result isn't automatically a good search result.
Test whether the correct information actually appears among the retrieved results.
How to Choose an Embedding Model
There's no universal "best embedding model."
Instead, evaluate candidates against your actual application.
A useful evaluation process is:
Collect Real Questions
↓
Define Expected Documents
↓
Generate Embeddings
↓
Run Retrieval
↓
Measure Results
↓
Compare Models
For example, you might create 50 real questions from your documentation.
Then measure:
Did the correct document appear?
Did the relevant chunk appear near the top?
Did similar questions retrieve similar information?
How fast was retrieval?
What did it cost?
How much storage did the vectors require?
This gives you evidence instead of relying on generic benchmark claims.
Embeddings and the Future of AI Search
Search is moving beyond simple keyword matching.
Modern AI applications increasingly combine multiple approaches:
Keyword Search
+
Semantic Search
+
Metadata Filtering
+
Reranking
↓
Better Retrieval
Embeddings are an important part of this shift.
They give applications a way to represent meaning mathematically and use that representation for search and retrieval.
But embeddings are only one piece of the larger retrieval puzzle.
As your RAG system grows, you'll need to think about how documents are stored, indexed, filtered, ranked, and evaluated.
That's where the next part of this series comes in.
Key Takeaways
Embeddings are numerical representations that allow AI systems to compare information mathematically.
For RAG, the basic flow is:
Document
↓
Chunk
↓
Embedding
↓
Vector
↓
Vector Database
When a user asks a question:
Question
↓
Query Embedding
↓
Similarity Search
↓
Relevant Chunks
↓
LLM
↓
Answer
Remember these core ideas:
Embeddings represent information as vectors.
Embeddings can capture semantic relationships.
Similar meanings can produce similar vector representations.
Embedding models create vectors.
Vector databases store and search vectors.
Both documents and queries can be converted into embeddings.
Cosine similarity is one common way to compare vectors.
Embedding dimension refers to the number of values in a vector.
More dimensions don't automatically mean better results.
Embeddings are used for search, recommendations, clustering, and many other AI applications.
Embeddings are a major building block of semantic retrieval.
Good embeddings can't compensate for poor chunking or bad retrieval.
Production systems should evaluate embedding models using real application data.
The most important idea is simple:
Embeddings give computers a mathematical way to compare information based on meaning.
And that's exactly what a RAG system needs when it tries to find the right information for a user's question.
FAQs
What are embeddings in AI?
Embeddings are numerical representations of information that capture useful relationships between data points. They allow AI applications to compare information mathematically.
What is a text embedding?
A text embedding is a vector representation of text generated by an embedding model. It can represent a word, sentence, paragraph, or larger piece of content.
Why are embeddings used in RAG?
Embeddings allow RAG systems to represent documents and user questions as vectors. The system can then compare those vectors to retrieve relevant information.
What is an embedding vector?
An embedding vector is an ordered collection of numbers representing information in a form that machine learning systems can process and compare.
What is embedding dimension?
Embedding dimension is the number of numerical values contained in an embedding vector.
What is cosine similarity?
Cosine similarity is a mathematical method for comparing the orientation of two vectors. It is commonly used to measure similarity between embeddings.
Are embeddings the same as semantic search?
No. Embeddings are numerical representations of information. Semantic search is a retrieval approach that uses meaning rather than relying only on exact keyword matches. Embeddings can be used to implement semantic search.
Do embeddings understand meaning?
Embeddings encode patterns and relationships learned by the model. This allows applications to compare pieces of information in ways that can reflect semantic similarity.
Are embeddings only used for RAG?
No. Embeddings are also used in semantic search, recommendation systems, document clustering, classification, duplicate detection, image search, and other AI applications.
Do I need a vector database for embeddings?
No. You can generate and compare embeddings without a vector database. However, vector databases are useful when you need to store and search large collections of vectors efficiently.
Conclusion
At first, an embedding can look like nothing more than a long list of numbers:
[0.12, -0.41, 0.73, 0.08, ...]
But those numbers are the foundation of an important idea in modern AI.
They give machines a way to represent and compare information based on relationships and meaning.
In RAG, that means a document can be converted into a vector, a user's question can also be converted into a vector, and the system can compare them to find relevant information.
The complete idea is:
Text
↓
Embedding
↓
Vector
↓
Similarity
↓
Retrieval
Once you understand embeddings, the next question naturally follows:
Where do all these vectors go, and how do we search through them efficiently?
That's the job of a vector database.
In the next article, we'll break down vector databases from the ground up, including how they store embeddings, how similarity search works, and where technologies such as pgvector, Qdrant, Milvus, Weaviate, and Pinecone fit into the RAG stack.
Next up: Vector Databases Explained.
Continue Learning
RAG becomes much easier to understand once you connect each concept to the next one.
Previous Article
What Is RAG? Retrieval-Augmented Generation Explained
Start here if you haven't read the first article. It explains the complete RAG architecture and how retrieval connects with LLM generation.
Next Articles
Learn where embeddings are stored, how vector search works, and why traditional databases aren't always enough for large-scale similarity search.
4. Semantic Search vs Keyword Search
See how semantic search differs from traditional keyword matching and why modern AI applications often combine both approaches.
5. How Document Chunking Works
Learn why documents are divided into smaller chunks and how chunk size can affect RAG retrieval quality.
6. Chunking Strategies for RAG
Explore fixed-size chunking, overlapping chunks, sentence-based chunking, and structure-aware approaches.
7. Build Your First RAG Application with Python
Put the concepts together and build a working RAG application using Python.
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