The core problem is that the standalone LLM is "hallucinating"—generating factually incorrect and insecure suggestions because its knowledge is static and outdated. The best architectural solution is to implement Retrieval-Augmented Generation (RAG). This approach connects the LLM to an external, continuously updated, and verified knowledge base (in this case, a security database). When a developer asks for code, the RAG system first retrieves the latest, most relevant security standards and library documentation from the database and then provides this information to the LLM as context. This "grounds" the model's response in factual, current data, directly mitigating hallucinations and ensuring its recommendations are secure and up-to-date.

A. Increase the temperature setting of the model's inference API: Increasing the temperature makes the model's output more random and creative. This would almost certainly increase the frequency of hallucinations and non-factual statements, worsening the problem.

B. Perform Adversarial Training using known Prompt Injection datasets: Adversarial training is a technique to make a model more robust against malicious inputs (Prompt Injection), not to correct its factual inaccuracies or update its internal knowledge base.

C. Deploy a Vector Database solely for storing historical model logs: Storing logs is essential for monitoring and auditing but does not provide the model with the correct, up-to-date information it needs to generate accurate responses. The model cannot learn from logs at inference time.

1. Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." Advances in Neural Information Processing Systems 33. This foundational paper introduces RAG as a method to combine parametric (internal) and non-parametric (external database) memory, enabling models to access and ground their outputs in up-to-date, factual information, thereby reducing fabrication. (Section 1: Introduction).

2. Gao, Y., et al. (2023). "Retrieval-Augmented Generation for Large Language Models: A Survey." arXiv preprint arXiv:2312.10997. Section 3.1, "Mitigating Hallucination," explicitly identifies the reduction of factual inaccuracies as a primary motivation for using RAG. It states that RAG provides access to real-time, verifiable information, which serves as a factual grounding for the LLM.

3. Amazon Web Services (AWS). (Official Vendor Documentation). "Retrieval Augmented Generation (RAG)." The documentation describes RAG as a technique to address the issue of LLMs generating incorrect information by providing them with information from an authoritative knowledge source, thus improving the quality and accuracy of responses.