N-Gram House

Tag: max tokens

Controlling Length and Structure in LLM Outputs: Practical Decoding Parameters

Controlling Length and Structure in LLM Outputs: Practical Decoding Parameters

Learn how to control LLM output length and structure using decoding parameters like temperature, top-k, top-p, and repetition penalties. Practical settings for real-world use cases.

Categories

  • Machine Learning (101)
  • History (50)
  • Business AI Strategy (34)
  • Software Development (25)
  • AI Security (21)

Recent Posts

Risk Management for Large Language Models: Controls and Escalation Paths Mar, 7 2026
Risk Management for Large Language Models: Controls and Escalation Paths
Data Privacy for Large Language Models: Principles and Practical Controls Mar, 11 2026
Data Privacy for Large Language Models: Principles and Practical Controls
Safety and Harms Evaluation for Large Language Models in Production: A Practical Guide Jun, 16 2026
Safety and Harms Evaluation for Large Language Models in Production: A Practical Guide
LLM Parameter Counts Explained: Why Size, Scale, and Architecture Matter Jul, 9 2026
LLM Parameter Counts Explained: Why Size, Scale, and Architecture Matter
Prompt Length vs Output Quality: LLM Decoding Tradeoffs Aug, 18 2026
Prompt Length vs Output Quality: LLM Decoding Tradeoffs

Menu

  • About
  • Terms of Service
  • Privacy Policy
  • CCPA
  • Contact

© 2026. All rights reserved.