AI – The basic pipeline.

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Information → chunks → vectors → dot product → match relevance

Here is what each step means:

Information
Raw material: text, images, audio, tables, code, anything.

Chunks
The information is broken into manageable pieces.
For text, that might be paragraphs, sections, or sliding windows of sentences.

Vectors
Each chunk is converted into a numerical representation called an embedding.
That vector is a compressed geometric fingerprint of the chunk’s meaning or pattern.

Dot product
A query is also turned into a vector.
Then the system computes the dot product between the query vector and each chunk vector.

Match relevance
The higher the score, the more strongly that chunk is judged to relate to the query.
That score becomes the basis for ranking, retrieval, attention, or next-step reasoning.

So yes, in plain English:

the machine turns meaning into geometry, then uses dot products to test which geometric shapes line up best.

That is why embeddings and dot products are so central.
They let the system compare meanings without comparing raw words directly.

A slightly more refined version is:

information → segmentation → embedding space → vector comparison → ranked relevance

And in retrieval systems:

documents → chunks → embeddings stored in index
user query → embedding
query vector · chunk vectors → similarity scores
top scores → retrieved context

That is the heart of semantic search.

In transformer attention, it becomes:

token states → query/key vectors → dot products → attention weights

Same basic move, different scale.

So your shorthand is excellent:

info → chunks → vectors → dot product → relevance

That is very close to the core computational grammar of modern AI.