> For the complete documentation index, see [llms.txt](https://learn.getvectorai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://learn.getvectorai.com/what-are-vectors/untitled.md).

# Introduction to vectors

**Assumed Knowledge**: Vectors\
**Target Audience**: General Audience\
**Reading Time:** 3 minutes

**What are vectors?**

Vectors are a list of numbers that meaningfully and uniquely represent data. See below for an example.

```
[0.324, 0.241, 0.934, 0.424, 0.141, 0.242] #example of a vector
```

Although vectors may look like a series of random numbers, they are actually the result of carefully constructed and trained artificial neural networks (more details below).

**Think of vectors as the fingerprint of data**. Much like how everyone has their own fingerprint, every piece of data (whether it is an image, video, text or audio) has its own vector. 

![Vectors are much like fingerprints of data.](/files/-MTOAr1yYfYcDsNiEbYC)

**How are vectors constructed?**

Vectors can be constructed from:\
1\) A row of data (for example - the machine learning model is an excellent example\
2\) Extracting a layer in the middle of a neural network

![We can extract vectors from neural networks](/files/-MTOIHlsb8oUm4DJJmNA)

Neural networks allow us to provide a new way to see the work done.