Prompt Best Practices
Best practices in Prompt Engineering
Be clear and specific
- Use clear, objective language.
- Avoid ambiguities.
- Specify what you expect as a response.
Example:
"List three benefits of using containers in software development."
Style: Provide context
- Give the model enough information to understand the scenario.
- Contextualize the question to avoid generic responses.
- Specify the language in which you want the response.
Example:
"In an agile development environment, what are the main benefits of using containers?"
"Help me explain the concept of AI with fewer technical terms for my MBA project."
"Explain this to me as if it were a topic in a children’s educational program, teaching elementary students."
"I am a software engineer using large language models for summarization. Summarize the following text in fewer than 250 words."
Formatting: Define the response format
- Specify the desired format, like lists, paragraphs, or examples.
Example:
"Explain the concept of microservices and provide three examples of its application in tech companies."
"Return as JSON."
Use examples
- Include examples in the prompt to guide the model toward more relevant answers.
Example:
"Explain the concept of DevOps and how it relates to CI/CD pipeline automation. For example, how can Jenkins be used in this context?"
Iterate and Refine
- Test different versions of the prompt to see which yields the best response.
- Adjust the prompt as needed to improve accuracy.
Restrict
- Limit options to make answers more precise.
Example:
Respond using only academic articles.
Don’t use sources older than 2020.
If you don’t know the answer, just say “I don’t know.”
Common Mistakes in Prompt Engineering
Overly Generic Prompts
- Avoid broad questions that may lead to vague responses.
Don't do it:
"What is AI?"
Do it:
"What is AI and how is it applied in image recognition?"
Ambiguous Prompts
- Avoid questions that could be interpreted in multiple ways.
Don't do it:
"Explain the use of networks."
Do it:
"Explain the use of neural networks in machine learning."