Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
Data Science
Towards Data Science·Jul 5·5 min read
Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs
TL;DR
A gap existed in efficiently transforming parsed questions into structured prompts for language models. A dispatcher was built to convert a ParsedQuestion into a typed call using a fixed base prompt and specific rules.
✦ Why It Matters
Engineers can implement a dispatcher to enhance prompt generation for language models, improving response accuracy.
Key Takeaways
- Implement a dispatcher to streamline prompt generation.
- Utilize ANSWER_REGISTRY for structured responses.
- Maintain a trace of interactions for validation.
- Combine fixed base prompts with specific rules for accuracy.
Full Summary
In the context of enterprise document intelligence, there was a need for a systematic way to generate prompts for language models based on parsed questions. A dispatcher was developed to take a ParsedQuestion and select the appropriate schema from an ANSWER_REGISTRY, combining it with a fixed base prompt and relevant fragments to create a user prompt.
This method ensures that each question is addressed with the correct structure, enhancing the reliability of the model's responses. The dispatcher also maintains a full trace of the interactions, which is crucial for validation and auditing purposes.
By implementing this structured approach, the system can efficiently handle various queries while ensuring that the responses adhere to predefined formats. This advancement is significant for engineers and researchers working on document intelligence applications, as it improves the accuracy and consistency of generated answers.