For anyone still unclear about Jev:

Prem Mishra — Tue, 22 Sep 2026 11:39:36 +0000

It’s an AI model designed to make decisions, not write paragraphs.

The reason it’s catching attention is simple: instead of an LLM giving your application text that it then has to interpret, Jev is designed to return a structured, type-safe answer that your code can directly work with.

LLM → generates text → software parses/interprets it → software acts on it
Jev → makes a structured decision → software acts on it

In that sense, Jev skips much of the interpretation layer.

Personally, I think it’s too early to know whether Jev itself will become crucial or widely adopted.

But the architecture behind it? I think that direction is here to stay.

Why Similarity Breaks Down at Scale

Prem Mishra — Mon, 21 Sep 2026 11:42:19 +0000

Embeddings don't store meaning. They store statistical proximity.

When you embed a phrase like "refund policy", the model isn't encoding what a refund actually is. It's placing that phrase in a high-dimensional space based on patterns learned from massive amounts of text.

The problem starts when that space gets large.

In 768 or 1536 dimensions, most vectors become surprisingly similar in distance.
This is the curse of dimensionality: as dimensions increase, the space expands so rapidly that the difference between relevant and somewhat related begins to shrink.

As a result, cosine similarity scores often cluster into a narrow range.

That's why a score of 0.85 can mean:
"This is exactly the document you need."
Or "This talks about the same topic but answers the wrong question."

The score itself isn't broken. Our interpretation of it is.

Similarity is not an absolute measure of relevance. It's a local signal that only makes sense within the context of a specific query and its neighbors.

This is why mature RAG systems don't rely solely on vector search. They calibrate thresholds, rerank results, and evaluate retrieval quality against real-world relevance metrics.

A vector tells you what's nearby. It doesn't tell you what's right.
That's the difference between retrieval that demos well and retrieval that works at scale.

DEV Community: Prem Mishra

For anyone still unclear about Jev:

Why Similarity Breaks Down at Scale