Tailoring prompts for specialized fields and industries
This chapter focuses on tailoring prompts for particular fields or industries, allowing you to leverage LLMs for specialized tasks and knowledge domains.
When using LLMs for code generation, it's important to provide clear context and specifications.
Best practices:
Specify the programming language and version
Provide context about the existing codebase or environment
Clearly define the desired functionality
Ask for comments and explanations within the code
Example prompt:
Task: Write a Python function (Python 3.8+) that calculates the Fibonacci sequence up to a given number of terms.Requirements:1. The function should take an integer parameter for the number of terms.2. It should return a list of Fibonacci numbers.3. Include error handling for invalid inputs.4. Add comments explaining the logic.Function signature: def fibonacci_sequence(n: int) -> List[int]:
For creative writing tasks, prompts should encourage imagination while providing enough structure to guide the output.
Best practices:
Specify the genre, style, or tone
Provide key elements (e.g., characters, setting, conflict)
Set constraints (e.g., word count, specific words to include)
Encourage unique or unexpected elements
Example prompt:
Write a 250-word flash fiction story with the following elements:Genre: Science fictionSetting: A colony on Mars, 100 years in the futureMain character: A botanist struggling to grow Earth plants in Martian soilConflict: Unexpected life forms appear in the greenhouseTone: Mysterious and hopefulMust include the words: "regolith," "photosynthesis," and "anomaly"Begin your story with: "The red dust settled on everything, even here in the sealed greenhouse."
When prompting for data analysis tasks, it's crucial to provide clear information about the dataset and desired insights.
Best practices:
Describe the dataset structure and key variables
Specify the type of analysis or visualization required
Ask for interpretations of the results
Request suggestions for further analysis
Example prompt:
You are a data analyst working with a dataset of customer information for an e-commerce company. The dataset includes the following columns: customer_id, age, gender, location, total_purchases, average_order_value, and customer_lifetime_value.Tasks:1. Suggest three meaningful visualizations that could provide insights into customer behavior and value.2. For each visualization, explain what insights it might reveal and how it could inform business decisions.3. Propose a segmentation strategy to categorize customers based on their value and behavior.4. Recommend additional data points that could enhance the analysis if collected in the future.Format your response as a structured report with clear headings for each task.
Now, let's practice creating domain-specific prompts:
Develop a prompt for generating a SQL query to analyze a hypothetical database of movie ratings and user information.
Create a prompt for writing a scientific abstract summarizing research findings in a field of your choice.
Design a prompt for generating a marketing email campaign for a new product launch, including specifications for tone, target audience, and key selling points.
Craft a prompt for an AI assistant specializing in financial advice, focusing on retirement planning for individuals in their 30s.
Example solution for #2:
Task: Generate a scientific abstract for a research paper in the field of renewable energy.Context: You are a researcher who has conducted a study on the efficiency of new organic photovoltaic materials for solar panels.Abstract structure:1. Background (1-2 sentences): Briefly explain the current state of organic photovoltaic technology and its importance.2. Objective (1 sentence): State the main goal of your research.3. Methods (2-3 sentences): Describe the key experimental techniques and materials used in your study.4. Results (2-3 sentences): Summarize the main findings, including quantitative data where appropriate.5. Conclusion (1-2 sentences): Interpret the significance of your results and their potential impact on the field.Additional requirements:- Use formal scientific language appropriate for a peer-reviewed journal.- Keep the total length between 200-250 words.- Include at least two relevant scientific terms or concepts specific to photovoltaic technology.- Avoid using first-person pronouns (I, we, our).Begin your abstract with: "Organic photovoltaic (OPV) materials offer a promising avenue for next-generation solar energy harvesting."
By mastering domain-specific prompting techniques, you can effectively leverage LLMs for a wide range of specialized tasks across various industries and fields of expertise. Remember to adapt your prompting strategies to the unique requirements and conventions of each domain.
In the next chapter, we'll explore prompt-based fine-tuning, which allows for even more specialized and accurate LLM outputs.