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
- Set clear instructions: Be explicit about the constraints you want the model to follow.
- Specify the format: Define the exact format or pattern you expect.
- Limit the length: Set boundaries on the response length, such as word or character counts.
- Use controlled vocabularies: Restrict the model to use only certain words or phrases.
- Provide templates: Offer a template that the model should fill in.
Example
Example 1: Binary Classification.
Example 1: Binary Classification.
Limiting the response to ‘Approved’ or ‘Denied’ ensures consistency and simplifies automated processing.Prompt:
Example 2: Short Answer Generation.
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:
Example 3: Technical writing.
Example 3: Technical writing.
Setting an exact word limit challenges the model to be concise and focus on the most important information.Prompt:
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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