Ayushman Saxena@i_m_ayush

We developed an AI-powered audit and verification tool for C++ smart contracts on Qubic, leveraging Retrieval-Augmented Generation (RAG) to interpret custom rulebooks and best practices dynamically. Key features include: Contract Rulebook Integration Store all smart contract guidelines, security best practices, and Qubic-specific patterns in a vector database (e.g., FAISS or Chroma) for retrieval during analysis. Contextual Code Analysis via RAG When analyzing smart contract files (e.g., HM25.h), the system automatically chunk the code, embed it, and retrieve the most relevant rules from the rulebook to provide highly contextual audits. LLM-Powered Auditing Use large language models (such as GPT-4, Claude, or Mistral) to review code chunks in the context of the retrieved rules, automatically identifying potential violations, unsafe patterns, or deviations from best practices. Human-Readable Explanations Instead of simply flagging issues, the system will generate clear, explanatory feedback about why a specific piece of code may be problematic according to the rulebook. No Manual Rule Encoding Required Rules and best practices are stored as documents, so new guidelines can be added or updated without changing any hard-coded static rules in the analyzer. Flexible Storage and Querying All rules are embedded in a vector store to enable semantic retrieval—ensuring that even loosely matching or complex queries can find relevant guidance. Integration with Developer Workflows Can be accessed via a web dashboard or CLI to upload C++ smart contract files and instantly receive a detailed audit report, streamlining code reviews and security checks. This approach aims to make Qubic smart contract auditing significantly smarter and more accessible by combining the power of natural language processing with structured rule-based guidance, without the need for extensive static rule maintenance.

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