springuni

A common gap on search teams: the relevance feedback loop isn’t actually closed.

Components often exist somewhere (query logs, some analytics, intuition-driven changes), but they don’t connect into a measure -> A/B test -> change -> re-measure cycle that protects or grows revenue.

This isn’t unique to search. I saw the same gap at other business problems. For search, it’s especially costly: relevance changes have an asymmetric downside, and the data needed to evaluate them usually already exists somewhere in the stack.

Question for anyone running a search system: how do you measure search relevance’s contribution to revenue?

Not asking to judge. Asking because I’m trying

I wrote a book on hybrid search because I couldn't find all of this in one place with the architecture details, evidence, and production context.

The most dangerous thing about vector search is that it never returns zero results. It always looks like it's working, even when it's confidently wrong.

Keyword search fails obviously. Vector search fails silently. That gap is where most production search problems live, and it's where this book starts.

"Designing Hybrid Search Systems" covers what blog posts and tutorials skip: the architecture decisions, tradeoffs, and failure modes that only surface in production.

20 chapters across six parts:
- Retrieval theory (why keyword and vector sea

I wrote a book on hybrid search because I couldn't find all of this in one place with the architecture details, evidence, and production context.

The most dangerous thing about vector search is that it never returns zero results. It always looks like it's working, even when it's confidently wrong.

Keyword search fails obviously. Vector search fails silently. That gap is where most production search problems live, and it's where this book starts.

"Designing Hybrid Search Systems" covers what blog posts and tutorials skip: the architecture decisions, tradeoffs, and failure modes that only surface in production.

20 chapters across six parts:
- Retrieval theory (why keywo

A keyword search that returns zero results is an obvious failure. The user reformulates, or you log it and add a synonym. Vector search never returns zero results. The nearest neighbor always exists. So when the system fails, it does so by confidently returning incorrect results that look identical to the correct ones at the API level.

A few failure modes I keep running into:

• Exact identifiers get smeared: A query for product SKU "XPS-13-9340" embeds near other XPS models, near other 13-inch laptops, near other Dell products. The retrieval looks confident, but it's wrong. BM25 would have either found the exact SKU or returned nothing, and "nothing" is a useful signal
• *Negation em

A keyword search that returns zero results is an obvious failure. The user reformulates, or you log it and add a synonym. Vector search never returns zero results. The nearest neighbor always exists. So when the system fails, it does so by confidently returning incorrect results that look identical to the correct ones at the API level.

A few failure modes I keep running into:

• Exact identifiers get smeared: A query for product SKU "XPS-13-9340" embeds near other XPS models, near other 13-inch laptops, near other Dell products. The retrieval looks confident, but it's wrong. BM25 would have either found the exact SKU or returned nothing, and "nothing" is a useful signal
• *Negation embeds

A keyword search that returns zero results is an obvious failure. The user reformulates, or you log it and add a synonym. Vector search never returns zero results. The nearest neighbor always exists. So when the system fails, it does so by confidently returning incorrect results that look identical to the correct ones at the API level.

A few failure modes I keep running into:

• Exact identifiers get smeared: A query for product SKU "XPS-13-9340" embeds near other XPS models, near other 13-inch laptops, near other Dell products. The retrieval looks confident, but it's wrong. BM25 would have either found the exact SKU or returned nothing, and "nothing" is a useful signal**.**
• *Negation e

I've been asked this question on an interview and I'll also share what I answered.

I would like to know if there's anything I haven't thought of?

GET /users/active=true
[{
    first_name: "Frank",
    family_name: "Felder",
    Birthday: "1985-01-12T11:20:54.000+01:00"
}]

My answer was the following:

(1) If you want to filter by a user being active, then that should be a parameter, like this: GET /users/?active=true.

(2) The naming of the keys in the returned JSON is inconsistent: Birthday should be birthday instead.

(3) For birthday specifically, using a timestamp isn't necessary, even confusing.

(4) Although it isn't relevant for the birthday specifically, the API

I've been asked this question on an interview and I'll also share what I answered.

I would like to know if there's anything I haven't thought of?

Python code:

nbResults: str = 45;

My answer was the following:

1. Storing an integer in a variable called str is a bad idea in a weakly-typed language

2. str is a built-in function in Python, using that name like that hides the original function

>>> str(45) '45'
>>> str=45
>>> str(45)
Traceback (most recent call last): File "<stdin>", line 1, in <module>
TypeError: 'int' object is not callable

3. Syntax is obviously wrong

• It could have been a function call like this:

nbResul

I've been asked this question on an interview and I'll also share what I answered.

I would like to know if there's anything I haven't thought of?

Question: Given the dataset (feature1, feature2, label) (0, 0, 0), (0, 1, 1), (1, 0, 1), (1, 1, 0), would you prefer to train a Logistic Regression or a Decision Tree Classifier for label prediction? Why?

Answer: If the dataset is just that, I wouldn't bother with training a classifier and introduce unnecessary complexity. It can be seen that `label = feature1 XOR feature2` is a correct and simple model.

Generally speaking, Logistic Regression is a great fit when the decision boundary is linear, ie. when data points can be separated

I've been asked this question on an interview and I'll also share what I answered.

I would like to know if there's anything I haven't thought of?

Question: Given the dataset (feature1, feature2, label) (0, 0, 0), (0, 1, 1), (1, 0, 1), (1, 1, 0), would you prefer to train a Logistic Regression or a Decision Tree Classifier for label prediction? Why?

Answer: If the dataset is just that, I wouldn't bother with training a classifier and introduce unnecessary complexity. It can be seen that `label = feature1 XOR feature2` is a correct and simple model. Generally speaking, Logistic Regression is a great fit when the decision boundary is linear, ie. when data points can be separated e

I've been asked this question on an interview and I'll also share what I answered.

I would like to know if there's anything I haven't thought of?

Problem: The probability of a pet being a dog is 40%. The probability that a pet dog weighs more than 2 kg is 70%. Given a pet is not a dog, the probability of it weighing less than or equal to 2 kg is 70%.

Question: What is the probability of a pet being a dog if it weighs more than 2 kg?

Solution:

(1) P(Pet = Dog) = 0.4

(2) P(Weight > 2 | Pet = Dog) = 0.7

(3) P(Weight ≤ 2 | Pet != Dog) = 0.7

One can obtain the answer by applying Bayes' Rule and the Total Probability Theorem, that is:

**P(Pet = Dog | Weight > 2) = P(Pet

I've been asked this question on an interview and I'll also share what I answered.

I would like to know if there's anything I haven't thought of?

Java code:

if(person.getName() = “jan”) {return true;}

My answer was the following:

1. If person is null, you'll get an NPE

2. Using = is wrong, even == is wrong for strings, we don't look for the same reference but for the same value, equals would be adequate here like this: "jan".equals(person.getName())

3. Something like person.getName() is rarely always lowercase, folks enter these sorts of things manually when they sign up. What if it's "Jan" instead or "JAN"? Should we return true then?

4. What kind of user experie