Hallucination

Mitigation strategy

1. **Implement Retrieval-Augmented Generation (RAG)** Architecturally integrate external, verified knowledge bases—such as internal documentation or trusted data sources—into the generation pipeline. This grounds the LLM's response in factual evidence, significantly reducing the reliance on the model's internal, potentially outdated, or fabricated training knowledge. RAG serves as the primary defense against ungrounded outputs by ensuring the model generates content strictly informed by the retrieved context. 2. **Employ Advanced Prompt Engineering and Reasoning Techniques** Utilize structured prompting methodologies, such as Chain-of-Thought (CoT) prompting, to compel the LLM to explicitly outline its step-by-step logical reasoning before generating a final answer. Concurrently, define strict output constraints within the prompt, for example, instructing the model to use *only* retrieved context or to explicitly reply with an "I don't know" or "Insufficient data" response when factual information is unavailable. 3. **Establish Contextual Guardrails and Human-in-the-Loop Verification** Deploy programmable safety guardrails that perform real-time contextual grounding checks post-generation to detect responses unfaithful to the provided source material. For high-stakes domains (e.g., legal, finance, healthcare), integrate a human-in-the-loop (HIL) workflow where outputs flagged by the guardrails as potentially hallucinatory are escalated for mandatory expert review and remediation before deployment or delivery to the end-user.