By Nofar Ben Ari
Publication Date: 2026-05-19 12:00:00
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…
