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top_p

6 articles · Page 1 of 1
AI Engineer Programming
AI Engineer Programming
Jun 11, 2026 · Artificial Intelligence

Understanding LLM Generation Parameters: Temperature, Top‑k, Top‑p, Penalties, and Max Tokens

The article explains how logits are transformed into probabilities via softmax and how generation parameters such as temperature, top‑k, top‑p, frequency‑penalty, presence‑penalty, and max_tokens intervene in the logits‑to‑sampling pipeline, detailing their mechanisms, common misconceptions, and practical limitations.

LLMTemperaturefrequency_penalty
0 likes · 15 min read
Understanding LLM Generation Parameters: Temperature, Top‑k, Top‑p, Penalties, and Max Tokens
Qborfy AI
Qborfy AI
Jun 9, 2026 · Artificial Intelligence

Deep Dive into Core LLM API Parameters

While many newcomers think using an LLM API is as simple as picking a model and feeding a prompt, the real control lies in parameters such as temperature, top‑p, top‑k, max_tokens, penalties, stop, and stream, each of which dramatically influences output quality, length, cost, and behavior.

APILLMPrompt Engineering
0 likes · 21 min read
Deep Dive into Core LLM API Parameters
Ops Community
Ops Community
Apr 21, 2026 · Artificial Intelligence

How to Tame Unstable LLM Prompts: Causes and Fixes

This article explains why large‑model prompts can yield inconsistent answers, examines the roles of temperature, top‑p/top‑k, tokenization, context windows, position bias, and model randomness, and provides a step‑by‑step debugging workflow and production‑grade best‑practice checklist to achieve stable outputs.

LLM stabilityPrompt EngineeringTemperature
0 likes · 13 min read
How to Tame Unstable LLM Prompts: Causes and Fixes
AI Algorithm Path
AI Algorithm Path
Mar 4, 2025 · Artificial Intelligence

How to Control LLM Output Using Temperature, Top‑K, and Top‑P

The article explains how sampling parameters—Temperature, Top‑k, and Top‑p—shape the output of large language models, comparing greedy and beam search, illustrating probability changes with concrete examples, and offering practical guidance on adjusting these settings for different tasks.

Beam SearchGreedy SearchLLM
0 likes · 9 min read
How to Control LLM Output Using Temperature, Top‑K, and Top‑P
Baidu Geek Talk
Baidu Geek Talk
Aug 21, 2023 · Artificial Intelligence

Decoding Strategies for Generative Models: Top‑k, Top‑p, Contrastive Search, Beam Search, and Sampling

The article explains how generative models use deterministic methods like greedy and beam search and stochastic techniques such as top‑k, top‑p, contrastive search and sampling, describing their mechanisms, temperature control, repetition penalties, and practical trade‑offs for balancing fluency, diversity and coherence.

.aiBeam SearchText Generation
0 likes · 9 min read
Decoding Strategies for Generative Models: Top‑k, Top‑p, Contrastive Search, Beam Search, and Sampling