← Anthropic Interview Insights
Went straight to temperature and top-p sampling, which is the obvious answer, but then they pushed further and I realized I hadn't thought carefully about the tradeoffs between them.
Start by defining creativity in generative AI as a spectrum from deterministic to highly stochastic outputs, then systematically cover the main techniques for control: sampling parameters, conditioning, architectural modifications, and post-hoc filtering. Emphasize trade-offs between creativity and coherence, and how to choose the right method based on application requirements and constraints.
Pro tip: Frame creativity control as a multi-dimensional problem—not just temperature—and discuss how you would evaluate and iterate on these controls in production, showing you think beyond one-off solutions.
Clarify what creativity means in the context of generative models: novelty, diversity, surprise, and coherence. Explain that control involves balancing these dimensions.
Discuss techniques like temperature scaling, top-k sampling, nucleus (top-p) sampling, and beam search. Explain how each affects the randomness and diversity of outputs.
Cover methods such as prompt engineering, classifier-free guidance, and control codes (e.g., style tokens) that steer generation without changing the model architecture.
Mention fine-tuning, reinforcement learning from human feedback (RLHF), and latent space manipulation (e.g., in VAEs or GANs) to inherently adjust creativity.
Describe filtering, reranking, and human-in-the-loop evaluation to ensure outputs meet creativity and quality standards. Highlight metrics like perplexity, diversity scores, and human judgments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.