Prompt engineering is crucial for getting the most out of language models like ChatGPT. Here are six best practices to help you craft effective prompts:
1. Be Clear and Specific:
- When creating prompts, clarity and specificity are key. Make sure your prompt is concise and clearly communicates the information you want from ChatGPT.
- Avoid ambiguous language and provide enough context to help the model understand your request.
Example:
- Less effective: “Tell me about AI.”
- More effective: “Explain the key principles of artificial intelligence and its applications in healthcare.”
- Real-world use case: A medical researcher might use ChatGPT to gather information on the latest AI advancements in diagnostics. A clear and specific prompt would be: “Summarize recent breakthroughs in AI-powered diagnostic tools for cancer detection.”
2. Use System Messages for Context:
- System messages can set the context for a conversation with ChatGPT. Provide a brief system message at the beginning to guide the model’s behavior throughout the interaction.
Example:
- System message: “You are an assistant that provides information about renewable energy sources.”
- Real-world use case: A marketing team working on a campaign for an electric vehicle company could use a system message like: “You are an assistant that offers insights on the benefits of electric vehicles and their impact on the environment.”
3. Experiment with Prompt Formats:
- Different prompt formats can yield different results. Try various formats such as questions, statements, or instructions to find what works best for your specific use case.
Examples:
- Question: “What are the benefits of solar energy?”
- Statement: “Discuss the advantages of solar energy.”
- Instruction: “List the top five benefits of solar energy.”
- Real-world use case: A content writer looking for inspiration on sustainable fashion might try different prompt formats like these.
4. Supply Examples and Desired Output Format:
- Articulate the desired output format through examples. Show the model what you expect.
Example:
- Less effective: “Extract the entities mentioned in the text below.”
- Better: “Extract the important entities mentioned in the text below. First extract all company names, then extract all people names, specific topics, and general overarching themes.”
- Desired format:
- Company names: <comma-separated list of company names>
- People names: -| |-
- Specific topics: -| |-
- General themes: -| |-
- Real-world use case: Extracting relevant information from a news article or research paper
5. Start with Zero-Shot, Then Few-Shot, and Consider Fine-Tuning:
- Begin with zero-shot prompts (no examples) and then try few-shot prompts (provide a couple of examples).
- If neither works, consider fine-tuning the model on specific tasks or domains
Example:
- Zero-shot: “Extract keywords from the below text.”
- Few-shot: “Extract keywords from the corresponding texts below.”
- Text 1: “Stripe provides APIs for payment processing.”
- Text 2: “OpenAI’s language models understand and generate text.”
- Real-world use case: Keyword extraction for content analysis.
6. Put Instructions at the Beginning of the Prompt:
- Clearly separate instructions from context using triple quotes or ###.
Example:
- Less effective: “Summarize the text below as a bullet point list of the most important points. {text input here}”
- Better: “Summarize the text below as a bullet point list of the most important points.”
- Text: “…”
- Real-world use case: Generating concise summaries
Remember, effective prompt engineering empowers you to guide the model and achieve better results.
