Explain Transformer Internals and Implement Top-p Decoding
Transformer Internals and Top-p Decoding
You are interviewing for an AI Scientist role. Explain the Transformer architecture in detail, including attention, positional encoding, encoder versus decoder usage, tokenization, and the shape flow through Q/K/V projections. Then describe how you would implement top-p sampling with softmax during LLM decoding.
Constraints & Assumptions:
- Focus on decoder-only LLMs unless the interviewer asks for encoder or seq2seq details.
- Use tensor shapes where useful.
- Top-p sampling should operate on one step of logits.
- Do not assume a specific framework beyond array/tensor operations.
AI interview prep
Core dilemma
Balancing theoretical understanding of complex neural network architectures (Transformer) with practical, implementable algorithmic details for a specific sampling strategy (top-p decoding).
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Explain Transformer Internals and Implement Top-p Decoding
Transformer Internals and Top-p Decoding
You are interviewing for an AI Scientist role. Explain the Transformer architecture in detail, including attention, positional encoding, encoder versus decoder usage, tokenization, and the shape flow through Q/K/V projections. Then describe how you would implement top-p sampling with softmax during LLM decoding.
Constraints & Assumptions:
- Focus on decoder-only LLMs unless the interviewer asks for encoder or seq2seq details.
- Use tensor shapes where useful.
- Top-p sampling should operate on one step of logits.
- Do not assume a specific framework beyond array/tensor operations.