Master Prompts Like a Pro in Minutes

How Prompt Engineering Works

Prompt engineering designs and optimizes prompts for language models. It’s important in NLP and language generation. Prompt formats guide the model and can be used for tasks like product descriptions or conversational AI. Reliable prompt formats exist, but exploring new formats is encouraged.

“(your input here)” is a placeholder for text or context.

Rules of Thumb and Examples

Rule #1

Instructions at beginning and ### or “”” to separate instructions or context

Rule #2

Be specific and detailed about the desired context, outcome, length, format and style.

Rule #3

Give examples of desired output format.

Rule #4

First try without examples, then try giving some examples.

Rule #5

Fine-tune if Rule #4 doesn’t work.

Fine-tuning improves model performance by training on more examples, resulting in higher quality results, token savings, and lower latency requests.

GPT-4 can intuitively generate plausible completions from few examples, known as few-shot learning.

Fine-tuning achieves better results on various tasks without requiring examples in the prompt, saving costs and enabling lower-latency requests.

Rule #6

Be specific. Omit needless words.

Rule #7

Use leading words to nudge the model towards a pattern.

Frequently Asked Question