Sampling and Decoding

Learning outcomes

• Explain probability reshaping
• Choose decoding settings
• Recognize reproducibility limits

Mental model

At each generation step the model produces scores over possible next tokens. Decoding converts that distribution into one continuation, trading determinism, diversity, and error risk.

Theory

Softmax turns logits into probabilities. Temperature rescales logits: lower values sharpen differences; higher values flatten them. Top-p keeps the smallest high-probability set whose cumulative mass reaches a threshold. Greedy decoding chooses the highest-probability token. Even deterministic settings may not guarantee bit-for-bit reproducibility across changing infrastructure or model versions.

Alternatives and trade-offs

Greedy decoding supports stable extraction, constrained decoding enforces syntax, sampling supports variety, and reranking can compare candidates. Settings must follow the task and evaluation evidence.

Failure modes and misconceptions

High temperature does not create knowledge, low temperature does not guarantee facts, and copying settings between models without evaluation is weak practice.

Knowledge check

Decision scenario

Use constrained structured output and conservative decoding for an invoice parser; use measured diversity for ideation, with separate factual verification.

Learning outcomes

• Explain Sampling and Decoding as a system mechanism rather than a slogan.
• Compare its alternatives, trade-offs, and production failure modes.
• Apply the concept to a decision and identify evidence that would validate it.

Trade-offs

Using Sampling and Decoding can improve capability or control, but it also introduces cost, latency, complexity, and failure modes that must be measured against an explicit objective.

Relationships

Primary sources

• Generation with LLMs — Hugging Face, verified 2026-07-16