Retrieval-Augmented Generation (RAG) systems often struggle in the final generation step, where hallucinations and misaligned citations degrade output quality. This post addresses three key areas: prompt design to guide the model toward faithful responses, hallucination suppression through constrained decoding and context grounding, and citation alignment to ensure every claim is traceable to retrieved documents. For developers building production RAG pipelines, these techniques are essential for reliability. The advice is actionable and grounded in real-world deployment experience, making it a valuable reference for AI engineers.
Practical techniques for optimizing RAG generation: prompt design, hallucination mitigation, and citation alignment.