GenPRD: An LLM workflow for automating mobile malware gap detection | IBM

The workflow is built using LangGraph and watsonx.ai LLM APIs. For this use case, we selected Meta‑LLaMA-3.3-70B-Instruct, balancing reasoning depth with practical performance. Below is a walkthrough of the core nodes in the flow:

Jira Extractor: This node uses the Jira API to read PRD content and split detection logic into smaller components based on logical OR operators.

PRD attributes Extractor: Using the LLM, this node converts logic components into structured JSON based on a predefined schema. It uses two separate prompts: one for numeric attributes and another for string attributes. The prompts include example inputs and corresponding outputs.

Gaps Extractor: Here, a deterministic function compares existing malware attributes with generated logic attributes. Any mismatches are recorded as gaps. If multiple logic components exist, the flow iterates accordingly.

Summarization Extractor: Finally, the LLM summarizes the detected gaps in clear, researcher‑friendly…