Understand inference and generation controls
See how decoding choices affect variation, length, and practical limits.
- Explain sampling, temperature, context limits, and latency tradeoffs.
At inference time, the model computes token scores and a decoding strategy selects the next token. Greedy decoding picks the highest-scoring option, while sampling draws from a probability distribution. Temperature changes how concentrated that distribution is: lower values generally make choices more concentrated, while higher values allow more variation. These controls change output patterns, not the underlying truth of a response.
A small example
scores = {"blue": 0.7, "green": 0.2, "red": 0.1}
chosen = max(scores, key=scores.get)
print(chosen)blue
Longer outputs require more token generation and can increase latency and cost. Context limits apply to the combined prompt and generated tokens. Streaming can display partial output sooner, but partial text may be incomplete; applications should handle cancellation, truncation, and malformed responses.
Key takeaways
Explain sampling, temperature, context limits, and latency tradeoffs.
Measure behavior with realistic examples and inspect important failures.
Lesson quiz
5 questions · pass with 4 correct · up to 50 XP
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