Enhance generation efficiency by splitting the task between two complementary models: a more powerful model that sets up the initial structure and key elements of the response, followed by a lighter, more efficient model that completes the details within these established constraints. This technique leverages each model's strengths while minimizing their weaknesses - the powerful model's ability to understand complex requirements and establish proper framing, combined with the lighter model's efficiency in generating consistent content within well-defined parameters. The system continuously monitors the quality of outputs against your scoring system to optimize the handoff point between models.
Constrained decoding represents an innovative approach to balancing quality with efficiency in AI systems. By learning to effectively combine models with different strengths, you'll develop skills in building hybrid systems that can achieve higher performance than either model alone while managing computational resources. This technique provides practical experience in model orchestration and helps you understand how different models can complement each other when properly constrained and directed.
Apply this technique when you need to maintain high-quality outputs while optimizing for cost and latency. For example, in an AI document summarization system, you might use a powerful model to analyze the document structure and generate key points, then hand off to a lighter model to expand these points into detailed paragraphs. The powerful model ensures proper framing and understanding, while the lighter model efficiently handles the detailed work within these constraints. This approach is particularly valuable when working with long-form content generation where maintaining consistency and structure throughout the output is crucial but the output is too long, making a large model generating the full output non viable from a cost and latency perspective. Use your scoring system to monitor quality at the handoff points and adjust the balance between models based on performance metrics.