Prompt engineering is a technique that is used to improve the performance of large language models such as GPT-3, ZenithAI and ChatGPT by fine-tuning the input data used to generate responses. This involves carefully selecting and structuring the input data in a way that maximizes its usefulness for the output of the model.
Prompt engineering can be done using a variety of techniques, such as:
- Wording: The wording of the prompt can be adjusted to influence the model’s output. For example, a prompt that asks the model to “generate a description of the product” may produce different results than a prompt that asks the model to “create an advertisement for the product”.
- Temperature: The temperature setting can be tweaked to control the randomness and creativity of the model’s output. A lower temperature means more predictable and conservative output, while a higher temperature means more diverse and novel output.
- Specificity: The prompt should be as specific as possible to avoid ambiguity and confusion for the model. For example, a prompt that asks for “a summary of an article” should also specify the length, format, and purpose of the summary.
- Explanation: The prompt can ask the model to explain its reasoning or provide evidence for its output. This can help improve transparency, accountability, and trustworthiness of the model’s output.
- Instruction: The prompt should put instructions at the beginning and separate them from the context. This can help clarify what task is expected from the model and what information is relevant for it.
- Examples: The prompt can give examples to show what format or style of output is desired. This can help guide the model’s generation process and reduce errors or inconsistencies.
Some general guidelines to improve accuracy when crafting prompts are:
- Use clear and simple language
- Avoid negations or double negatives
- Use keywords or phrases that are relevant to the task
- Use punctuation and capitalization properly
- Avoid slang or idioms
Prompt engineering is a powerful technique that can be used to improve performance of AI models by providing them with better-quality input data. It involves techniques such as data preprocessing, feature selection, data augmentation which can be used to make most out of limited data available & improve performance of models . By applying these techniques, one can leverage large language models like GPT-3 & ChatGPT for various tasks such as text summarization, text generation, question answering, sentiment analysis, etc.