AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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PLLM+, a hybrid dependency-repair pipeline evaluated on the HG2.9K benchmark, suggests that deterministic reuse of previously validated dependency configurations is a simple and effective strategy, with LLM-based repair serving as a secondary fallback for cases not covered by prior solutions.
The Static-Pass Dynamic-Fail (SPDF) phenomenon and a three-stage agentic pipeline combining static scanning, LLM-driven Common Weakness Enumeration (CWE) reasoning, and autonomous exploit verification in isolated Docker containers are introduced.
Overall, prompt structure improves compliance but is an unreliable substitute for robust security controls in LLM-assisted development.
This tutorial introduces a reusable, end-to-end evaluation pipeline grounded in empirical software engineering practices, focusing on post-generation validation rather than prompt design, allowing for validating AI-generated code in modern development workflows.
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