N-Gram House

Tag: LLM output control

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 (125)
  • History (50)
  • Business AI Strategy (50)
  • Software Development (39)
  • AI Security (28)

Recent Posts

Retention and Deletion Policies for LLM Prompts and Logs Sep, 10 2026
Retention and Deletion Policies for LLM Prompts and Logs
Compute Budgets and Roadmaps for Scaling Large Language Model Programs Jun, 8 2026
Compute Budgets and Roadmaps for Scaling Large Language Model Programs
Debugging Prompts: Systematic Methods to Improve LLM Outputs Apr, 5 2026
Debugging Prompts: Systematic Methods to Improve LLM Outputs
Documentation Architecture: Using ADRs and Decision Logs for AI-Generated Systems May, 19 2026
Documentation Architecture: Using ADRs and Decision Logs for AI-Generated Systems
How to Forecast Delivery Timelines with Vibe Coding Data Jan, 23 2026
How to Forecast Delivery Timelines with Vibe Coding Data

Menu

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

© 2026. All rights reserved.