There has been a lot of buzz in recent years about the potential of large language models (LLMs) to develop new text forms, translate languages, compose various types of creative material, and answer your queries in an instructive manner. However, one of the drawbacks of LLMs is that they may be quite unexpected. Even little changes to the prompt might provide drastically different outcomes. This is where quick engineering comes into play.
The technique of creating prompts that are clear, explicit, and instructive is known as prompt engineering. You may maximise your chances of receiving the desired outcome from your LLM by properly writing your questions.
Given below are some of the techniques you can use to create better prompts:
- Be precise and concise: The more detailed your instruction, the more likely your LLM will get the intended result. Instead of asking, "Write me a poem," you may say, "Write me a poem about peace".
- Use keywords: Keywords are words or phrases related to the intended outcome. If you want your LLM to write a blog article about generative AI, for example, you might add keywords like "prompt engineering," "LLMs," and "generative AI."
- Provide context: Context is information that assists your LLM in comprehending the intended outcome. If you want your LLM to write a poetry about Spring, for example, you might add context by supplying a list of phrases around Spring.
- Provide examples: Use examples to demonstrate to your LLM what you are looking for. For example, if you want your LLM to create poetry, you may present samples of poems you appreciate.
Andrew NG has created an online course to learn about prompt engineering here - https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
In fact, the rise of LLMs has resulted in new job roles like "Prompt Engineer" as highlighted in the articles below:
