Take the intimidating thing apart. Keep the part that carries the meaning.

How does a computer know that “dog” and “puppy” are related when they are different words?

Semantic search finds information by comparing meaning rather than only matching words.

How does a search engine decide which document is most relevant — using nothing but word counts?

BM25 ranks a document by how often your words appear in it, weighted by how rare those words are, corrected for document length.

Why does a search system that understands meaning still miss the answer that was right there in the document?

Chunking is the decision about what counts as one searchable unit — and most retrieval failures start there.

If vector search already found the right document, why do production systems pay for a second pass?

A reranker is a slower, more careful reader that looks at your query and each candidate together — and fixes the order the fast pass got approximately right.

How do you know a retrieval system got better — without trusting your own vibes?

Retrieval metrics are three questions asked of one ranked list: did you find it, how high, and how well did you order everything that matters?

What if a model answered with a decision and a probability instead of a paragraph?

Jev is a System One model that returns typed decisions with calibrated probabilities, turning “which passages should reach the model?” into a policy you can measure.

If you set temperature to 0, do you get the same answer every time?

Temperature rescales the model's next-token probabilities. Zero means always take the most likely token, one means sample the model's own distribution, but absolute determinism is a property of the whole stack, not a dial.

What do you actually lose when you shrink an embedding from 1536 numbers to 256?

You are not deleting most of the meaning; you are lowering the resolution of the semantic space, and the first thing to blur is the difference between things that are almost the same.

Why can a model that writes working code not count the letters in a word?

A language model does not read characters; it reads tokens, so counting letters is a reconstruction task, not a lookup.

Why does doubling the context length quadruple the work?

Attention lets every token look at every previous token, so the work grows with the square of the context, while the weights in each row must still sum to one.