To implement custom scoring or ranking with OpenAI’s outputs, you’ll need to design a workflow that evaluates the model’s responses based on your specific criteria. Start by generating multiple candidate responses from the API (using parameters like n to produce several completions). Then, apply your own scoring logic to each response, and select the one that best aligns with your requirements. This approach allows you to combine OpenAI’s generative capabilities with your domain-specific rules or preferences.
For example, suppose you’re building a support chatbot where responses must include troubleshooting steps. You could generate five completions using n=5, then score each based on criteria like keyword presence (“restart,” “check settings”), clarity (sentence length), and structured formatting (numbered steps). A Python script could iterate through the responses, assign points for each criterion met, and select the highest-scoring option. You might also use regex patterns to detect required phrases or call external APIs—like a profanity filter—to add penalties for unwanted content. Tools like cosine similarity (via libraries like sentence-transformers) could help compare responses to ideal templates.
Key considerations include balancing computational cost (generating multiple responses increases API calls) and ensuring your scoring logic is robust. For instance, a scoring function that prioritizes brevity might inadvertently favor incomplete answers. To mitigate this, combine multiple metrics (e.g., penalizing answers below a word count while rewarding keyword inclusion). Testing with real-world data is critical: run your scoring system against manually labeled examples to refine weights and thresholds. If performance is a concern, start with fewer candidates (e.g., n=3) and optimize your scoring code for speed (e.g., caching embeddings). This hybrid approach lets you maintain control over output quality while leveraging the model’s creativity.