Interactive Code Documentation Chatbot With Retrieval Grounding

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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.

Innovation Solution

A deep-learning based chatbot system that utilizes metadata and user-specific inputs to generate tailored documentation, incorporating code snippets and natural language processing to provide accurate responses based on user profiles and knowledge levels, using retrieval and interactive documentation models to avoid hallucinations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If standard user profile documentation is used, then documentation can be provided quickly, but it limits applicability and understanding for users with varying expertise levels

Engineering Contradiction:

Improvedocumentation provision speedVSAvoidapplicability to different user expertise levels

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic documentation generation that adapts to each user's expertise level in real-time. The system adjusts the complexity, depth, and style of documentation based on user profile attributes, transforming static standard documentation into dynamic personalized content that maintains speed while improving adaptability

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes multiple parameters of documentation including complexity level, technical depth, example detail, and explanation style based on user expertise level. By varying these parameters dynamically, the system provides appropriate documentation depth for each user without sacrificing generation speed

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If large deep-learning models are used to provide documentation, then comprehensive responses can be generated, but accuracy and relevance may deteriorate due to hallucinations

Engineering Contradiction:

Improvecomprehensiveness of documentation responsesVSAvoidaccuracy and relevance of responses

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent introduces an intermediary retrieval model that sits between the user query and the documentation generation process. This intermediary retrieves relevant ground truth information from codebase and documentation sources, ensuring that the final documentation response is based on accurate, verified information rather than purely generated content, thus eliminating hallucinations while maintaining comprehensiveness

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12487818B2Interactive chatbot documentation

Publication Date: 2025.12.02 SERVICENOW INC

AI summary

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.