AI Prompt Generator

A good AI prompt has four elements: Role (tell the AI who it is), Task (what to do), Context (relevant background), and Format (how to structure the output). The RTCF framework consistently produces better results than vague one-liners.

Prompt engineering is the practice of crafting inputs to AI language models to get more accurate, useful, and consistent outputs. It involves techniques like few-shot examples, chain-of-thought reasoning, and structured formatting. Better prompts mean better results without changing the model.

Core principles (clear task, role, context, format) apply across all LLMs. However, models have different strengths: Claude tends to follow detailed instructions precisely; GPT-4 is strong at creative tasks; Gemini is better with Google data. A prompt may need minor tuning when switching models.

1–3 examples usually give the biggest improvement. Beyond 5 examples, gains diminish and you consume more context. Use examples that are diverse, high-quality, and in the same format as your task. Zero-shot is fine for simple tasks; few-shot is essential for tasks with specific style or domain conventions.