Global Araç
Ai Sampling Settings Helper
Bir kullanım alanı seçin ve OpenAI tarzı API'ler için önerilen bir örnekleme yapılandırması alın. Değerler başlangıç noktalarıdır—buradan ince ayar yapın.
{
"temperature": 0.9,
"top_p": 0.95,
"top_k": 80,
"presence_penalty": 0.6,
"frequency_penalty": 0.3
}Sampling settings — temperature, top_p, top_k, frequency_penalty, presence_penalty — control how an LLM picks each next token from its predicted probability distribution. They're the difference between a model that produces deterministic, focused output (low temperature, restrictive top_p) and one that produces creative, varied output (high temperature, looser top_p). Wrong settings for your use case is one of the most common and easily-fixed quality issues in LLM deployments. A creative-writing app at temperature 0 will produce flat, formulaic output. A code-generation tool at temperature 1.2 will produce hallucinated, syntactically broken code.
The helper takes your use case (code generation, creative writing, factual Q&A, summarization, classification, role-play, translation, JSON output, data extraction) and returns recommended settings with brief rationale. Code generation: temperature 0.0-0.2, top_p 1.0 (you want the most probable tokens — deviation breaks syntax). Factual Q&A: temperature 0.0-0.3 (low variance, accurate retrieval). Creative writing: temperature 0.7-1.0, top_p 0.9 (variety without total chaos). Brainstorming: temperature 1.0-1.5 (maximum exploration). JSON / structured output: temperature 0.0 (deterministic format adherence). Role-play / character: temperature 0.7-0.9, presence penalty 0.3-0.6 (variety without repetition).
Two parameter relationships worth understanding: (1) Temperature and top_p interact — if both are restrictive, output is very narrow; if both are loose, output is chaotic. Most practitioners pick one to control and leave the other at default (temperature is more intuitive). (2) Frequency_penalty (penalizes tokens by how often they've appeared) and presence_penalty (penalizes tokens that appeared at all) help with repetition in longer outputs — useful for poetry, storytelling, brainstorming where you want variety; harmful for technical writing where repetition of correct terms is desired. Defaults of 0 are usually right unless you're seeing repetitive output.
Nasıl Kullanılır
- Pick your use case from the menu (code, creative writing, Q&A, JSON, etc.).
- Read the recommended temperature, top_p, and penalty settings.
- Read the brief rationale for why those values fit your case.
- Apply the values in your API call (Anthropic, OpenAI, Google all use similar parameter names).
- Iterate: if output is too varied, lower temperature; too repetitive, raise temperature or add presence_penalty.
Ne Zaman Kullanılır
- Setting up a new LLM-powered feature and choosing initial sampling parameters.
- Debugging quality issues — wrong temperature is often the cause when output feels off.
- Comparing config across providers (Anthropic, OpenAI, Google all use these parameters with subtle differences).
- Onboarding new engineers to LLM API usage — one quick reference for typical settings.
Ne Zaman Kullanılmaz
- Some endpoints don't support all parameters (e.g., Claude doesn't expose top_k by default in API).
- Reasoning models (o1, o3, Sonnet extended-thinking) handle their own internal sampling — most parameters have limited or no effect.
- Fine-tuned models often need different settings than their base — don't blindly apply defaults.
- When the underlying issue is prompt quality, not sampling — fixing temperature can't compensate for a bad prompt.
Yaygın Kullanım Senaryoları
- Verifying a number or output before passing it on
- Quick use during a typical workday
- Pre-decision sanity-check on inputs and outputs
- Educational use — demonstrating the underlying concept
Sık Sorulan Sorular
What's the difference between temperature and top_p?
Temperature reshapes the probability distribution (low temp = sharper / more confident, high temp = flatter / more random). Top_p (nucleus sampling) truncates the distribution to the smallest set of tokens whose cumulative probability is p (e.g., 0.9 = top tokens that together cover 90% of probability mass). Best practice: pick one to control. Temperature is more intuitive; top_p is more precise.
What does temperature 0 actually do?
Greedy sampling — the model always picks the highest-probability next token. This is deterministic given the same prompt and same model version (sometimes called 'greedy decoding'). NOT necessarily 100% reproducible across model updates or even minor floating-point differences in some implementations. For maximum reproducibility, also set seed if your provider supports it.
How do penalties work?
Frequency_penalty subtracts a value from the probability of each token in proportion to how often it's appeared in the output so far. Presence_penalty subtracts a fixed value once a token has appeared, regardless of frequency. Both range -2.0 to 2.0 in OpenAI's API. Positive values discourage repetition; negative encourage it. Use 0.3-0.6 for creative writing to add variety; leave at 0 for technical content.
Should I change settings for different prompts?
Often yes. A single API endpoint serving multiple feature areas (code, creative, JSON) should adjust temperature per request type. Most production apps that get this right have a config map: {code: 0.0, creative: 0.8, json: 0.0, summary: 0.3} and pick based on the operation being performed. Hardcoding one temperature for all uses is a common mistake.
What about top_k?
Top_k restricts sampling to the K most probable next tokens. Less commonly used than top_p but works similarly — both prune the distribution. Anthropic's Claude API supports top_k; OpenAI doesn't expose it. Useful for constraining wild sampling at high temperatures: temperature 1.0 + top_k 40 gives variety with a safety floor on coherence.
Are these settings the same across providers?
Mostly. Temperature, top_p, presence_penalty, frequency_penalty work similarly across Anthropic, OpenAI, Google, DeepSeek with subtly different default ranges. Claude defaults to temperature 1.0; GPT defaults to 1.0; Gemini defaults to 0.7-ish. Always check provider docs — values you set should match across providers but defaults often differ. Reasoning models (o1, o3, Claude with extended thinking) override these with internal logic.