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What are constrained outputs

Constrained outputs involve instructing the LLM to generate responses that adhere to specific limitations or formats. This could mean setting a word limit, specifying a response type (like “yes” or “no”), or requiring the output to match a particular pattern or structure.

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How to implement constrained outputs

  1. Set clear instructions: Be explicit about the constraints you want the model to follow.
  2. Specify the format: Define the exact format or pattern you expect.
  3. Limit the length: Set boundaries on the response length, such as word or character counts.
  4. Use controlled vocabularies: Restrict the model to use only certain words or phrases.
  5. Provide templates: Offer a template that the model should fill in.

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Example

Limiting the response to ‘Approved’ or ‘Denied’ ensures consistency and simplifies automated processing.Prompt:
Review the following application and respond with 'Approved' or 'Denied' only.

Application Details: [Applicant's information and criteria]

Decision:
By specifying that the answer should be in one sentence, you prevent the model from providing overly long or off-topic responses. Prompt:Prompt:
Based on the text below, answer the question in one sentence.

Text: 'The Great Barrier Reef is the world's largest coral reef system located in Australia.'

Question: 'Where is the Great Barrier Reef located?'

Answer:
Setting an exact word limit challenges the model to be concise and focus on the most important information.Prompt:
Summarize the following article in exactly 50 words.

[Insert article text]

Summary (50 words):

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Why use constrained outputs

  • Increase precision: Helps the model provide exactly what you need without unnecessary information.
  • Enhance consistency: Ensures uniformity across multiple outputs, which is crucial for tasks like data entry or form filling.
  • Simplify parsing: Makes it easier to programmatically process the responses.
  • Reduce errors: Minimizes the chance of irrelevant or incorrect information creeping into the output.
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