Controlling Output Length and Format

Once you understand how to give basic instructions using prompts, you can refine them to gain more control over the Large Language Model's output. Two significant aspects you can influence are the length and the structure, or format, of the generated text. Getting the model to produce text that fits your specific length requirements or adheres to a desired layout is often important for practical applications.

Techniques for Controlling Output Length

LLMs don't have built-in word counters they strictly follow like a word processor, but you can guide them towards generating responses of a particular length using clear instructions within your prompt.

1. Be Explicit About Length: The most direct method is to simply ask.

   • Summarize the previous paragraph in exactly one sentence.
   • Write a product description of about 50 words.
   • Explain the concept of photosynthesis in a single paragraph.
   • List the steps, keeping each step under 10 words.

2. Specify the Number of Items: For lists or points, asking for a specific number is effective.

   • What are the 3 main advantages of using Python?
   • Give me 5 ideas for blog posts about renewable energy.

3. Use Relative Terms (Use with Caution): Terms like "briefly," "short," or "detailed" can influence length, but they are subjective and the LLM's interpretation might vary. Explicit constraints are usually more reliable.

   • Briefly explain what an API is. (Expect a shorter answer than just asking Explain what an API is.)
   • Provide a detailed explanation of the water cycle. (Expect a longer answer.)

Keep in mind that LLMs generate text probabilistically, often word by word (or token by token). While they try to follow length constraints, they might not be perfectly precise, especially with character or exact word counts. However, providing these constraints significantly increases the likelihood of getting an output closer to your desired length.

Techniques for Controlling Output Format

Beyond length, you often need the LLM's response in a specific structure. You can guide the model to produce formatted output through careful prompting.

1. Request Specific Formats: Directly tell the model how you want the output structured.

   • List the planets in our solar system as a bulleted list.
   • Provide the contact information (name, email, phone) in JSON format.
   • Write the instructions using numbered steps.
   • Generate a Python function that adds two numbers. (Implies code formatting).
   • Format the key differences as a table with two columns: Feature and Description. (Note: Complex table generation in plain text can be challenging for LLMs).

2. Use Examples (Few-Shot Prompting): As discussed previously, providing examples is a powerful way to show the model the exact format you expect.

   • Prompt:

     Translate the English word to French:
     
     English: cat
     French: chat
     
     English: dog
     French: chien
     
     English: house
     French: ?
     

     The structure of the examples guides the model to simply provide the French word.

   • Prompt:

     Extract the name and job title from the text. Format as JSON.
     
     Text: "Sarah Chen is the Lead Data Scientist at TechCorp."
     Output: {"name": "Sarah Chen", "job_title": "Lead Data Scientist"}
     
     Text: "The project manager, David Lee, will contact you."
     Output: ?
     

     This clearly demonstrates the desired JSON structure.

3. Structure Your Prompt: Sometimes, the way you structure the prompt itself can hint at the desired output format.

   • Prompt: Provide pros and cons for electric cars.\n\nPros:\n- \n\nCons:\n- By starting the list structure, you encourage the model to continue it.

Combining Length and Format Control

You can combine these techniques in a single prompt to control both aspects simultaneously.

• List the 3 main benefits of exercise in a numbered list.
• Generate a short summary (2 sentences maximum) of the article below.
• Provide 5 keywords related to "machine learning" as a comma-separated list.
• Create a JSON object containing the 'title' and 'author' of 3 classic science fiction books.

Iteration is Normal

Getting the length and format exactly right might take a few tries. If the first response isn't quite what you wanted, refine your prompt. Be more specific, adjust your constraints, or add clearer examples. For instance, if "Summarize in one paragraph" yields too much text, try "Summarize in 3-4 sentences." Experimenting with different phrasings is a standard part of interacting effectively with LLMs.

By clearly communicating your requirements for length and structure within the prompt, you can guide LLMs to generate text that is not only relevant but also presented in the way you need it.