A beginner’s guide to Instructor: Get Structured Outputs from LLMs
📰 Dev.to · Srujana Maddula
Learn to get structured outputs from LLMs using Instructor, a framework for controlling LLM outputs
Action Steps
- Install the Instructor library using pip
- Import the Instructor library and load a pre-trained LLM model
- Define a custom prompt template using Instructor's API
- Pass the prompt to the LLM model and get a structured output
- Configure the output format to match your application's requirements
Who Needs to Know This
Data scientists, AI engineers, and software engineers can benefit from using Instructor to get structured outputs from LLMs, making it easier to integrate LLMs into their applications
Key Insight
💡 Instructor helps control LLM outputs, making it easier to integrate them into applications
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Get structured outputs from LLMs using Instructor!
Key Takeaways
Learn to get structured outputs from LLMs using Instructor, a framework for controlling LLM outputs
Full Article
LLMs generate text by predicting the next best token. Pass the same prompt twice and you might get...
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