Large Language Models (LLMs) such as GPT, Claude, and Gemini provide developers with a powerful array of parameters, which are **tunable settings that shape the model’s output.** These parameters govern how much randomness is allowed, how long the response should be, whether the output should follow specific rules or formats, and more. Understanding these parameters is essential for fine-tuning LLM behavior across applications like writing assistants, chatbots, data extractors, and reasoning agents. ## **1. What Are LLM Parameters?** Parameters are **settings that control how an LLM generates its response**. Think of them as dials on a control panel. Adjusting them doesn’t change the model’s knowledge, but it changes how the model expresses that knowledge. You can: - Make the model more creative or more focused - Control repetition or enforce structure - Decide whether it explains its thinking - Instruct it to stop when a specific phrase is reached - Request structured formats like JSON Here is an overview of the core parameters: | **Parameter** | **Short Description** | | -------------------- | ------------------------------------------------ | | `temperature` | Controls randomness/creativity | | `top_p` | Limits choices by cumulative probability | | `max_tokens` | Caps total output length | | `stop` | Defines strings that end output generation | | `frequency_penalty` | Reduces repeated word frequency | | `presence_penalty` | Discourages repeated word usage | | `seed` | Fixes randomness for reproducible output | | `response_format` | Sets output format (text, JSON, etc.) | | `tools` | Lets model use external tools or APIs | | `structured_outputs` | Enforces structured output layout | | `top_k` | Limits choices to top-k most likely tokens | | `min_p` | Requires minimum probability for token selection | | `top_a` | Adaptive sampling combining top-k and top-p | | `repetition_penalty` | Penalizes all repeated tokens | | `logit_bias` | Boosts or blocks specific token likelihoods | | `logprobs` | Returns log-probabilities of chosen tokens | | `top_logprobs` | Shows top-N alternative token choices | | `include_reasoning` | Prompts model to show its thinking | | `reasoning` | Enables chain-of-thought response strategy | | `web_search_options` | Controls how the model performs web searches | ## **2. How to Set and Test Parameters** LLM parameters are typically set through **API calls** or **developer platforms** such as: **1. OpenAI API ([https://platform.openai.com](https://platform.openai.com))** You configure parameters like `temperature`, `max_tokens`, and `stop` in the JSON payload when making API requests. **2. Anthropic Claude API** (https://docs.anthropic.com) Claude supports a similar set of controls with its own parameter syntax. Other main models use a similar approach. **3. OpenRouter.ai** ([https://openrouter.ai](https://openrouter.ai)) This platform supports multiple models (OpenAI, Mistral, Anthropic, etc.) and allows you to set parameters directly through a unified API or web interface. **4. Hugging Face Transformers** In local or hosted deployments, parameters can be set using the `generate()` method on model objects. To test parameter settings: - Run side-by-side prompts with different parameter values - Use fixed `seed` for reproducibility - Use `logprobs` to evaluate token confidence - Log outputs for batch comparisons ## **3. Frequently Used Parameters** These are the most commonly used parameters in production applications, prototypes, and developer tools. They offer direct control over output randomness, length, repetition, and format. ### **3.1. Temperature** - **What it does:** Controls the level of randomness in the model’s output. - **Technical Explanation:** The model predicts tokens with certain probabilities. Temperature adjusts the sharpness of that distribution: - A low temperature (e.g., 0.0–0.3) makes the model deterministic, always choosing the most likely word. - A high temperature (e.g., 0.7–1.2) introduces variability, encouraging creativity or exploration of less obvious outputs. - **ELI5:** It’s like a spice level. A low temperature is plain rice. A high temperature is a spicy curry - less predictable, more interesting. - **Typical Use Cases:** - Low temp: code generation, factual QA - High temp: storytelling, brainstorming ### **3.2. Top-p (Nucleus Sampling)** - **What it does:** Controls how many token choices the model considers, based on cumulative probability. - **Technical Explanation:** With `top_p = 0.9`, the model only samples from the top tokens whose total probabilities add up to 90%. This narrows randomness to only relatively likely options. - **ELI5:** Instead of picking from every possible word, the model only picks from the "popular crowd" - the top few that together account for most of the smart guesses. - **Typical Use Cases:** Used with or instead of temperature for more focused creativity. In other words, when an LLM generates a word (token), it doesn't just choose randomly, it looks at a list of possible next words with assigned probabilities based on context. But you don’t always want the model to consider every possible next word and that’s where top-k and top-p come in. They are sampling filters: - Top-k: limits the number of choices to a fixed number of tokens (more about that later). - Top-p: limits the number of choices based on a fixed cumulative probability. It includes as many tokens as needed to pass the probability threshold. ### **3.3. Max_tokens** - **What it does:** Sets the maximum number of tokens the model is allowed to generate. - **Technical Explanation:** A token is usually a word or word part. This parameter limits output length for performance, cost, or UI reasons. - **ELI5:** It’s like telling a kid: “You can only say 50 words.” They’ll stop speaking once they reach the limit. - **Typical Use Cases:** - Short responses (max_tokens: 50–100) - Articles or essays (max_tokens: 500–1500) ### **3.4. Stop** - **What it does:** Defines strings that signal the model to stop generating further output. - **Technical Explanation:** When the model produces any string in the stop list, it ends the output immediately. Useful for delimiting sections or enforcing boundaries. - **ELI5:** It’s like a secret word. When the robot says or sees that word, it knows to stop talking. - **Typical Use Cases:** Dialog turns, structured formats, stopping at “User:” or “\n\n”. ### **3.5. Frequency_penalty** - **What it does:** Penalizes words that appear frequently in the generated text. - **Technical Explanation:** Reduces the chance of repeated words by subtracting a value from their probabilities each time they’re reused. Higher penalty = less repetition. - **ELI5:** Imagine a robot keeps saying “great.” This setting tells it, “You’ve said that too much. Try something else.” - **Typical Use Cases:** Longer outputs, creative writing, marketing copy. ### **3.6. Presence_penalty** - **What it does:** Penalizes words that have already been used, even once. - **Technical Explanation:** A lighter control than frequency. Once a token is used, it’s discouraged from being used again. Promotes novelty and exploration. - **ELI5:** If you’ve already said “apple,” try not to say it again. Say “banana” or “grape.” - **Typical Use Cases:** Idea generation, descriptions, poetry. ### **3.7. Seed** - **What it does:** Ensures deterministic results for the same input and parameters. - **Technical Explanation:** Fixes the random number generator used in sampling. Perfect for A/B tests or reproducible research. - **ELI5:** If you roll the same loaded dice with the same hand, you’ll get the same number every time. - **Typical Use Cases:** Testing, debugging, reproducible demos. ### **3.8. Response_format** - **What it does:** Determines whether the output is raw text, JSON, HTML, or another format. - **Technical Explanation:** Specifies the structure of the output, helping ensure machine-readability in structured applications. - **ELI5:** It’s like asking a friend to reply in a paragraph, a list, or a table. - **Typical Use Cases:** APIs, structured extraction, function-calling. ### **3.9. Tools** - **What it does:** Lets the model call external functions or services. - **Technical Explanation:** Enables tool use like calculators, web search, or custom APIs. Controlled through configuration. - **ELI5:** It’s like giving the robot a phone so it can call your calculator or ask Google. - **Typical Use Cases:** Assistants, agents, RAG pipelines, data enrichment. ### **3.10. Structured_outputs** - **What it does:** Forces the model to generate a structured response (e.g., JSON, field list). - **Technical Explanation:** Combines formatting constraints with semantic fields for reliable integration with other systems. - **ELI5:** “Don’t tell me a story. Give me boxes filled with answers.” - **Typical Use Cases:** Data pipelines, coding agents, workflows. ## **4. Rarely Used Parameters** These parameters are powerful but less frequently needed. They are mostly useful in research, experimentation, or advanced control scenarios. ### **4.1. Top-k** - **What it does:** Restricts token selection to the top `k` highest probability options. - **Technical Explanation:** Instead of looking at probabilities cumulatively (like `top_p`), it just picks the top `k` tokens and ignores the rest. - **ELI5:** “Pick your next word from this top 50 list only.” - **When to use:** Rarely, in performance-sensitive or legacy systems. ### **4.2. Min_p** - **What it does:** Discards tokens below a certain minimum probability threshold. - **Technical Explanation:** If no token exceeds the `min_p`, the model falls back to a default behavior. This limits low-confidence token selection. - **ELI5:** “Only speak if you're sure — at least 60% sure!” - **When to use:** In high-confidence output generation or constrained environments. ### **4.3. Top_a** - **What it does:** Provides adaptive filtering for next-token selection. - **Technical Explanation:** A hybrid of `top_k` and `top_p` but adaptively selects a subset based on both. - **ELI5:** “I’ll pick the best few guesses — not too few, not too many — just enough to make sense.” - **When to use:** Primarily in experimental models or research systems. ### **4.4. Repetition_penalty** - **What it does:** Penalizes all tokens that have previously appeared, using a global multiplier. - **Technical Explanation:** Lowers the logits of repeated tokens based on how often they appeared, regardless of context. - **ELI5:** “Every time you repeat a word, your voice gets quieter.” - **When to use:** More common in Hugging Face-style LLMs. ### **4.5. Logit_bias** - **What it does:** Adjusts the likelihood of specific tokens before they’re considered for generation. - **Technical Explanation:** Gives you direct control over token selection: - Positive values make tokens more likely - Negative values make them less likely - -100 disables a token entirely - **ELI5:** “Never say ‘banana,’ and always start with ‘Hello.’” - **When to use:** For censorship, structured responses, or templating. ### **4.6. Logprobs** - **What it does:** Returns log-probabilities of each generated token. - **Technical Explanation:** Helps interpret model confidence and selection behavior. - **ELI5:** “How sure were you about each word you said?” - **When to use:** Evaluation, scoring, research. ### **4.7. Top_logprobs** - **What it does:** Returns the most likely alternative tokens for each position. - **Technical Explanation:** Useful for understanding near-miss completions or choice diversity. - **ELI5:** “What else could you have said instead of this word?” - **When to use:** Prompt tuning, debugging, AI interpretability. ### **4.8. Include_reasoning / Reasoning** - **What they do:** Encourage the model to reason out its answers step-by-step. - **Technical Explanation:** Influence internal prompting mechanisms to generate chain-of-thought or structured explanations. - **ELI5:** “Don’t just give the answer. Show your work.” - **When to use:** In math, logic, educational tasks. ### **4.9. Web_search_options** - **What it does:** Tells the model how to use the web (in browsing-enabled setups). - **Technical Explanation:** You can restrict domains, time ranges, or result counts. - **ELI5:** “You can Google something, but only look at the top 3 results from news sites.” - **When to use:** Live assistants, news analysis, research tasks. ## **5. Why Parameters Matter?** Understanding and tuning LLM parameters transforms a generic model into a precise, reliable, and adaptable assistant. With this guide, you now have a detailed roadmap of each parameter, when to use it, and how to use it correctly.