Understanding Embeddings easily.

I've been hearing about embeddings for a while now, and even as someone who's very conversant with using LLMs as a daily driver and for integrating into smart systems, I wasn't really sure what exactly embeddings were and how they connected with everything else.

In this writeup, I'll be unpacking some of the things I've been able to learn about embeddings — what they are and how to use them as a software developer/engineer.

Turning Meanings into Coordinates

Think of embeddings as turning meanings into coordinates. LLMs are not built to — and cannot — understand words the same way humans do, so they convert text into lists of numbers that represent meaning.

Take the word "dog" for example. An LLM wouldn't straightforwardly understand what the word means until it converts it into a group of numbers:

"dog" → [0.21, -0.88, 0.44, ...]

What the Numbers Are NOT Based On

The number of values in an embedding has nothing to do with:

• Word length


• Number of letters


• Number of characters

This is because embeddings don't encode spelling — they encode meaning and features. The embedding size is determined by:

• The embedding model's architecture


• How much semantic information the model wants to represent

So the embedding dimension is directly proportional to the model size.

Key Properties

• Similar meanings end up close together
  


• Different meanings end up farther apart
  

You could say that embeddings are basically "a mathematical location for meaning."

Real-World Analogy

Imagine a large city map:

• Tailors live in one district


• Doctors live in another district


• Developers live in a separate district

Now replace people with words, sentences, documents, or even images. That's basically embeddings!

A few more examples to drive it home:

Why Do Embeddings Matter?

Embeddings are what make AI able to:

• 
  Search semantically — find results based on meaning, not just keywords


• Recommend similar content


• Retrieve relevant context


• Power Retrieval-Augmented Generation (RAG) systems


• 
  Compare meanings instead of exact words

Case Study: Semantic Search

Without embeddings, AI search would behave like old-school keyword search — returning results based on exact phrase matching.

With embeddings, a query like "How to fix app crashing" would also surface results like:

• "Application keeps closing"


• "React Native app freezes"


• "Unexpected mobile app shutdown"

...because the meanings are close, even if the words are different.

What Can Be Embedded?

Almost anything:

• 
  Words — e.g., "King"


• 
  Sentences — e.g., "How to build a React app"


• 
  Entire documents — e.g., PDFs, docs, chats, codebases, etc.


• 
  Images — this is how Google reverse image search works

What Happens Behind the Scenes?

The system compares embeddings using similarity/distance metrics:

• 
  Cosine similarity — measures how similar two embeddings are based on their direction, regardless of size. If two vectors point almost the same way, they likely have similar meaning.


• 
  Euclidean distance — measures the actual straight-line distance between two embeddings in vector space. A smaller distance means the meanings are closer together.

Applying Embeddings in RAG

Let's look at how embeddings fit into a RAG (Retrieval-Augmented Generation) pipeline. Here's an example: building an enhanced search engine for a company website.

Step 1 → Convert documents into embeddings

         (e.g., PDFs, notes, product catalogs, support docs)



Step 2 → Store them in a vector database

         (e.g., Pinecone, Weaviate, Chroma, PGVector)



Step 3 → A user asks: "How do suppliers onboard?"



Step 4 → The question is converted into an embedding too



Step 5 → The system searches for nearby embeddings

         (semantically similar documents)



Step 6 → Relevant chunks are sent to the LLM

This is essentially how most "Chat with your docs" implementations work.

A Common Misconception

Some people think embeddings store knowledge — but that's not quite right.

Embeddings store:

• Semantic relationships


• Meaning patterns

The actual reasoning still happens in the LLM. Embeddings mainly help the model find relevant information, not process it.

Embedding Models

Open-Source / Free

These can be downloaded, run locally, fine-tuned, and used without API costs:

Closed / Paid APIs

These are accessed through APIs and are typically billed per token or request:

If you've read to this point — congratulations, you're already on your way to becoming a pro RAG engineer. (Just kidding.)

Thanks for reading through though.