🍧 Prompt Engineering Best Practices

Abstract

This chapter presents the best practices of Prompt Engineering.

Best Practices of Prompt Engineering

As discussed earlier, Prompt engineering is an iterative process of refining prompts. For a prompt engineer, it is crucial to be aware of the best practices of prompt engineering to save time and effort. Here are some best practices for prompt engineering:

1️⃣ Understand Model Capabilities and Limitations

Familiarize yourself with the strengths and weaknesses of the language model you're working with. Understand what types of queries it performs well on and where it might struggle. This will help you craft the prompt to align with the LLM's capabilities and avoid asking for something it cannot do.

2️⃣ Provide Context

With proper context, the model can better understand the user requirements clearly and then generate a relevant response. Otherwise, the generated response may not be relevant.

3️⃣ Use Clear and Concise Language

Employ clear, concise, and unambiguous language in the prompt. Avoid jargon, overly technical terms, or complex sentence structures that might confuse the LLM.

4️⃣ Specify Instructions and Expectations

Clearly state the instructions and expectations for the desired output. Be specific about the format and tone of the output you want the LLM to generate.

5️⃣ Break Down Complex Tasks

For complex tasks, consider breaking them down into smaller, more manageable steps. This can help the LLM process the task more effectively and provide more focused responses.

6️⃣ Use Examples

When required, provide examples or demonstrations of the desired output to give the LLM a clearer understanding of what you expect. Show the desired format or structure by presenting similar examples.

7️⃣ Leverage Advanced Prompting Techniques

Explore advanced prompting techniques like chain-of-thought prompting or tree-of-thought prompting for tasks that require more complex reasoning or hierarchical structures.

8️⃣ Start with Simple Prompts

Begin with straightforward and simple prompts to understand the model's baseline behavior. This can help you identify any unexpected issues or biases in its responses.

9️⃣ Experiment, Iterate and Refine Prompts

Prompt engineering is an iterative process. Experiment with different prompts, input variations, and approaches to see what yields the best results. Don't hesitate to iterate based on the model's responses.

🔟 Control Output Length

If you need a response of a specific length, explicitly specify it in the prompt.

1️⃣1️⃣ Temperature and Max Tokens

Adjust the temperature parameter to control the randomness of the model's output. Lower values (e.g., 0.2) make the output more deterministic, while higher values (e.g., 0.8) introduce more randomness (i.e., more creative).

1️⃣2️⃣ Address Biases and Sensitivity

Be aware of biases that might be present in the model's training data. If you encounter biased or sensitive responses, it is recommended to refine your prompt.

1️⃣3️⃣ Regularly Update Prompts

Language models can be fine-tuned and updated over time. Regularly review and update your prompts to align with the model's evolving capabilities and any changes made by the developers.

1️⃣4️⃣ Do Post-Processing

When required, do post-processing to enhance the quality of the generated content. This can help ensure accurate and reliable outputs.

1️⃣5️⃣ Consider Fine-Tuning

Consider fine-tuning the model on relevant data in case of specific use cases or domains. This can help tailor the model to specific tasks and improve its performance.