Seed
Also called seeds
The starting value of a random number generator. The same seed and settings reproduce the same samples on the same setup.
Interview questions about Seed
- Compare PCA and autoencoders as ways to fight the curse of dimensionality, and explain how you'd tell which compression helps downstream.
In high dimensions data gets sparse and distances concentrate, so models need far more data.
- Implement a local training pipeline for a text-embedding recommender built from member profiles, item text and click or skip interactions, with leakage-resistant splits and reproducible inference.
Join each impression to the profile as of that timestamp, label clicks positive and skips negative, then split by time: train on the past, validate on the…
- Your integration tests call a hosted model at temperature 0 and compare the output with an exact string. They fail now and then. What's going on, and what do you change?
The test assumes a determinism a hosted API doesn't promise. Temperature 0 is greedy and uses no random numbers, and our sampling script gave byte-identical…
- Your LLM gives different answers to the same input. How would you make inference reproducible, and how many repeated runs would you need for a stable score?
First remove the randomness you control: greedy decoding or a fixed seed, and a pinned model, prompt and parameters.
- Build a lightweight evaluation harness for a model served through Ollama: run a dataset of prompts, score the answers against references and report the results.
Keep it small: a JSONL dataset, a client that calls Ollama's generate endpoint with temperature fixed, a scorer, and a report.
- You're handed a noisy dataset for LLM post-training. How do you decide whether it helps, and what evidence would you show?
Treat it as an experiment. Fix the base model, recipe and a held-out eval suite for target skills plus general regressions.
- Build a pipeline that finds math documents in web crawls and scores their quality.
Three stages. Recall: cheap filters for LaTeX or MathML markup, math symbols and known math domains, with extraction that keeps equations, which standard…
- Implement K-means with Lloyd's algorithm. What does it optimise, how do you initialise it, and what do you do when inference data doesn't look like training data?