AI Prompts
Build, test, and optimize prompts for large language models. Includes system prompt builders, few-shot example generators, chain-of-thought helpers, and prompt libraries for common use cases.
Build structured AI prompts with role, task, context, and output format fields.
Analyze how complex your AI prompt is and understand each contributing factor.
Clean and format AI prompts by removing invisible characters and normalizing whitespace.
Check if your prompt fits within a model context window and get compression tips.
Analyze and improve AI prompts with rule-based suggestions.
Split long prompts into chunks that fit within model context windows.
Build structured system prompts for Anthropic Claude using XML tags.
Build structured prompts for Google Gemini AI models.
Build structured prompts in LLaMA instruct format with <<SYS>> and [INST] tags.
Build structured system prompts for ChatGPT and GPT-4o.
FAQ
- What is prompt engineering?
- Prompt engineering is the practice of crafting inputs to AI models to achieve desired outputs. Good prompts include clear instructions, relevant context, output format specifications, and examples when needed.
- What is a system prompt?
- A system prompt is a set of instructions given to an AI model before the user conversation begins. It sets the persona, behavior, output format, and constraints for the model throughout the session.
- What is few-shot prompting?
- Few-shot prompting provides the model with a small number of input-output examples before the actual task. This guides the model to follow the same pattern for new inputs, improving consistency and accuracy.