Chatbot Documentation Retrieval for Accurate API Answers
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Solution Overview
Problem
Existing software and API documentation is often tailored to a standard user profile, limiting its applicability and can be difficult for users with varying levels of expertise to understand efficiently, and large deep-learning models may provide inaccurate or irrelevant responses due to hallucinations.
Innovation Solution
A deep-learning based chatbot system that utilizes metadata and trained machine learning models to tailor documentation responses to user profiles, ensuring accuracy by using retrieval models to refine searches and generate coherent responses based on user knowledge levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If documentation is tailored to a standard user profile, then it can be easily maintained and structured, but it limits applicability and efficiency for users with varying levels of expertise
Solution Approach 1:
The documentation system dynamically adapts to user expertise levels by using machine learning models to analyze user profiles and retrieve relevant documentation sections. The system transitions from static standard-profile documentation to dynamic personalized documentation delivery based on real-time user characteristics.
Solution Approach 2:
The system changes the parameter of documentation detail level based on user expertise. By adjusting the amount and depth of information provided according to user profile parameters, the system delivers appropriate documentation complexity without requiring multiple complete documentation sets.
2Productivity
If comprehensive documentation is provided to cover all user expertise levels, then all users can find relevant information, but users must sift through unnecessary details reducing efficiency
Solution Approach 1:
The system extracts only the relevant documentation sections needed for each user based on their expertise level. The retrieval model identifies and extracts specific documentation portions that match user needs, eliminating unnecessary information from the delivery while maintaining comprehensive coverage in the documentation repository.
Solution Approach 2:
The system applies partial action by providing only the necessary portion of documentation for each user rather than the complete documentation set. This reduces information overload for users while ensuring all necessary information is available in the full documentation repository for when needed.
3Adaptability or versatility
If large deep-learning models are used to provide documentation responses, then comprehensive answers can be generated, but inaccuracies and hallucinations occur
Solution Approach 1:
The system introduces a retrieval model as an intermediary between the user query and the deep-learning model. The retrieval model first identifies relevant documentation sections, which then serve as grounded input for the language model, ensuring responses are based on actual documentation rather than model hallucinations.
Solution Approach 2:
The system performs preliminary retrieval of relevant documentation sections before generating responses. This preliminary action ensures that the deep-learning model works with verified, relevant information rather than generating responses from scratch, reducing hallucinations while maintaining comprehensiveness.
Data Source
AI summary
In the present application, a method for providing interactive documentation based on a chatbot and deep-learning based techniques is disclosed. Metadata associated with program code documentation is identified, wherein the program code documentation is associated with corresponding program code. A natural language question regarding the corresponding program code is obtained via a virtual agent. A response to the natural language question is determined based on the metadata using one or more trained machine learning models. The response to the natural language question is provided to the virtual agent.