Retrieval-Augmented Generation (RAG) systems combine retrieval and generation to produce accurate, reliable AI outputs. However, a key challenge is ensuring that the generated content is properly grounded in the retrieved documents and that citations are trustworthy.
To improve grounding, start by enhancing the retrieval stage. Use hybrid retrieval combining sparse (e.g., BM25) and dense (e.g., embedding-based) methods to capture both keyword and semantic relevance. This reduces the chance of missing critical context.
Next, refine the generation stage by explicitly prompting the model to cite sources. For example, instruct the LLM to output a reference marker (e.g., [1]) for each claim and then append a reference list at the end. Fine-tune the model on examples that demonstrate proper citation behavior.
Implement chunking strategies that preserve context — avoid splitting sentences or tables. Use overlapping chunks to ensure continuity. For long documents, adopt a hierarchical retrieval approach: first retrieve relevant sections, then drill down into specific paragraphs.
Add a verification step after generation. Deploy a separate model or rule-based checker to verify that each cited fact appears in the retrieved documents. Flag uncited or mismatched claims for human review.
Finally, log citation metadata (chunk ID, document source) and monitor citation accuracy in production. Use feedback loops to iteratively improve retrieval and generation.