Prompt Optimiser-Efficient Prompt Refinement

Example

Original prompt: 'Could you possibly explain to me how quantum computing works, if it's not too much trouble?'. Optimized prompt: 'Explain quantum computing in simple terms.'

Scenario

This function is applied when users include overly polite or convoluted language that can obscure their actual query. The optimisation makes the prompt more straightforward, improving the LLM's ability to understand and respond accurately.

Example

Original prompt: 'I need a summary of the latest research findings on climate change for a general audience.'. Optimized prompt: 'Summarize the latest climate change research findings for a general audience.'

Scenario

Used when the user's request must be tailored for a specific audience, ensuring the response is appropriate in terms of complexity, terminology, and tone.

Example

Original prompt: 'How can I implement a machine learning model to predict stock prices?'. Optimized prompt: 'Step 1: Explain how to choose a machine learning model for stock price prediction. Step 2: Describe the data preprocessing steps. Step 3: Guide on model training and evaluation.'

Scenario

This function is vital for complex inquiries, where it divides the task into manageable steps, facilitating a more structured and comprehensive response from the LLM.