A Prompt Tester lets you experiment with different prompts and parameters to see how an LLM would respond — without consuming API credits or requiring an internet connection. It's essential for prompt engineering, helping you craft effective prompts before deploying them in production. By simulating responses locally, you save costs and iterate faster.

Temperature (range 0–2) controls randomness in the output. Lower values (0–0.3) produce more deterministic, focused responses — ideal for factual Q&A and code generation. Higher values (0.7–1.5) introduce creativity and variation, useful for brainstorming and creative writing. Values above 1.5 may produce nonsensical outputs. The default of 0.7 balances coherence with creativity.

The System Prompt sets the overall behavior, tone, and constraints for the AI (e.g., "You are a medical expert. Only provide evidence-based answers."). It acts as a persistent instruction layer. The User Prompt is the specific query or task you want the AI to address. Together, they form the complete context that the model uses to generate its response. Mastering both is key to effective prompt engineering.

Tokens are chunks of text that models process — roughly 4 characters or 0.75 words per token in English. Most LLM APIs charge per token (both input and output). Our estimator uses character count ÷ 4 for a quick approximation. Understanding token usage helps you manage costs and stay within model context limits (e.g., GPT-4's 128K context window).

GPT-4 Turbo: Best for complex reasoning, nuanced analysis, and high-stakes tasks. GPT-3.5 Turbo: Great balance of speed and capability for most tasks. Claude 3 Sonnet: Excellent for long-form content and safety-conscious applications. Claude 3 Haiku: Fast and cost-effective for simple tasks. Gemini Pro: Strong multimodal capabilities. Llama 3 70B: Best open-source option with impressive performance. Use this simulator to compare response styles across models.

Frequency Penalty (0–2) discourages the model from repeating the same words or phrases, promoting vocabulary diversity. Presence Penalty (0–2) discourages the model from mentioning topics it has already covered, encouraging it to explore new concepts. Use frequency penalty to reduce redundancy, and presence penalty to broaden the scope of responses.