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Core
Logits, Log-Probs, and Logit Bias
The output layer of an LLM is an API surface, not just an implementation detail. Logits are raw per-token scores, softmax turns them into probabilities, and log-probs are what providers actually return because they are numerically stable and add up across a sequence. Four production techniques live here: confidence scoring for routing and abstention, logit bias to ban or force a token, structured decoding by masking invalid tokens, and classification by reading a single position's log-probs instead of parsing prose. AI, ML, and GenAI engineer interviews probe it because it is the difference between treating the model as a text box and treating it as a probabilistic component.
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LLM & GenAI FundamentalsWhat are logits and log-probs, and how do you actually use them in production?→Machine Learning & Data ScienceHow do you handle multiclass classification (softmax vs one-vs-rest vs one-vs-one)?→LLM & GenAI FundamentalsYour LLM classifier's accuracy swings when you reword the prompt or reorder the labels. How do you fix it?→LLM & GenAI FundamentalsWhy do transformers scale attention scores by 1/√d_k, and what breaks if you skip it?→Coding & DSAImplement a numerically stable softmax and cross-entropy loss from scratch.→System Design for AI in ProductionDesign a real-time content moderation system for text and images at platform scale.→
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