What are Embeddings?

You spend a lot of time trying to get people to understand your business. You pitch investors, you explain features to customers, and you write documentation for employees. Communication is the bedrock of a startup.

But when it comes to software, communication usually breaks down. Computers do not understand language. They understand math. If you type the word “apple” into a database, the computer sees a string of characters. It does not see a fruit. It does not know that it is related to “pie” or “orchard.”

This limitation has plagued software for decades. It is why standard search bars on websites are often terrible. If a user searches for “cheap sedan” and your inventory lists “affordable car,” a standard keyword search might miss the connection entirely.

Embeddings solve this translation problem.

They are the bridge between human meaning and machine understanding. If you are building a product that involves text, images, or user recommendations, you are likely going to need embeddings. They are not just a buzzword for data scientists. They are a fundamental building block for modern software architecture.

Understanding them allows you to make better decisions about your tech stack and the capabilities of your product.

At its core, an embedding is a list of numbers. In technical terms, we call this a vector. These numbers act as coordinates.

Imagine a standard graph with an X and a Y axis. You place a dot on the graph to represent a specific data point. Now, imagine a graph with hundreds or even thousands of dimensions instead of just two. This is the vector space.

When we create an embedding for a piece of data, like a word or a sentence, we are assigning it a specific location in this multi-dimensional space. The magic happens in how those locations are determined.

The system places similar concepts close together.

If you were to visualize this space, you would find the point for “dog” sitting very close to the point for “puppy.” The point for “cat” would be nearby, but perhaps slightly further away than “puppy.” The point for “motorcycle” would be in a completely different part of the graph.

This proximity represents semantic meaning.

By turning data into coordinates, computers can calculate the distance between two concepts. If the distance is short, the concepts are related. If the distance is long, they are unrelated.

This allows the software to understand context. It no longer looks for exact character matches. It looks for mathematical closeness.

Most founders are familiar with keyword matching. This is how CTRL+F works on a webpage. It is deterministic and rigid.

If you search for “running shoes,” a keyword system looks for that exact phrase. If your product description says “jogging sneakers,” the system fails. You miss the sale.

Embeddings operate on probability and relatedness rather than exact matches. This is often referred to as semantic search.

Here is how they compare in a startup context.

Precision

• Keywords provide high precision for exact terms (searching for a specific part number).
• Embeddings provide high recall for intent (searching for “something to fix a leaky pipe”).

  

Flexibility

• Keywords are brittle. A typo or a synonym breaks the search.
• Embeddings are resilient. They capture the “gist” of the input.

Complexity

• Keywords are cheap and fast to implement. Every database supports them.
• Embeddings require a vector database and an inference step to generate the vectors.

For a startup, the choice is not always one or the other. Many successful systems use a hybrid approach. They use keywords to filter down the massive dataset and embeddings to rank the results by relevance.

The utility of embeddings goes far beyond just a better search bar. They enable features that used to require teams of manual reviewers or complex rule engines.

Recommendation Engines

You do not need to be Spotify to build a recommendation engine. By converting user behavior or product details into embeddings, you can instantly find “nearest neighbors.” If a user likes Product A, the system looks at the vector space to find which products are mathematically closest to Product A.

Classification

Startups often deal with messy data. Customer support tickets are a prime example. You can use embeddings to automatically tag incoming emails. The system converts the email text into a vector and compares it to clusters of known issues like “billing,” “bugs,” or “feature requests.” This automates triage without writing thousands of if-then rules.

RAG (Retrieval-Augmented Generation)

This is currently the most popular use case for startups building on top of Large Language Models (LLMs). LLMs like GPT-4 can hallucinate. They also do not know your private business data.

RAG allows you to take your internal documents, turn them into embeddings, and store them. When a user asks a question, you search your vector database for relevant chunks of text. You then feed those chunks to the LLM and say, “Answer the user’s question using only this context.”

This makes the AI accurate and specific to your business.

While embeddings are powerful, they introduce new variables to your business model. It is important to look at the practical downsides.

There is a cost to generation. Every time a user inputs text or you add a new record to your database, you must send that data to a model (like OpenAI’s text-embedding-3 or an open-source alternative) to turn it into numbers. This adds latency and API costs.

There is also the issue of dimensionality. Earlier models used fewer dimensions. Modern models use thousands. Storing millions of vectors with thousands of dimensions requires significant memory. Standard databases often choke on this workload, leading to the rise of specialized vector databases like Pinecone, Weaviate, or pgvector.

You also have to ask how you handle updates. Meaning changes over time. If your startup pivots or the vocabulary of your industry shifts, your old embeddings might become stale. You may need to re-embed your entire dataset.

Are you prepared to maintain a pipeline that keeps these mathematical representations in sync with reality?

Embeddings are a layer of abstraction. They abstract away the messiness of human language into clean, calculable math. For a founder, they are the tool that lets you stop building rigid, fragile software and start building systems that can interpret intent.

The math is complex, but the application is straightforward. It is about moving from matching strings to matching meaning.