Use constrained outputs

Set clear boundaries and rules for the model’s responses to improve accuracy, consistency, and utility

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.

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.

Example

Example 1: Binary Classification.

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:

Example 2: Short Answer Generation.

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:

Example 3: Technical writing.

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):

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.