What strategies can improve the coherence of a RAG answer if the retrieved passages are from different sources or have different writing styles (the “frankenstein” answer problem)?

To address the “frankenstein” problem in RAG systems—where answers feel disjointed due to varied sources or writing styles—three key strategies can improve coherence: context unification during retrieval, controlled generation with explicit instructions, and post-generation refinement. These methods focus on harmonizing content before, during, and after the generation phase to produce a seamless output.

First, context unification involves preprocessing retrieved passages to align terminology and structure. For example, if one passage uses “machine learning model” and another refers to “AI algorithm,” the system could standardize terms to “model” before passing data to the generator. Tools like named entity recognition (NER) or custom synonym mappings can automate this. Additionally, re-ranking passages based on stylistic similarity (e.g., prioritizing academic vs. conversational tones) ensures the generator receives more consistent input. For instance, a retrieval step might filter out overly technical jargon if the target audience expects simpler explanations. This reduces the burden on the generator to reconcile conflicting styles.

Second, controlled generation uses prompt engineering to guide the model toward synthesizing information cohesively. Explicit instructions like, “Combine the following sources into a single explanation using neutral, professional language,” direct the model to act as an editor rather than a passive summarizer. For example, if sources disagree on a fact (e.g., conflicting dates for an event), the prompt could include, “Resolve discrepancies by prioritizing the most recent data.” Fine-tuning the generator on datasets that require style harmonization—such as merging news articles and research papers—can also improve its ability to unify content. Developers might also limit the model’s tendency to mimic source-specific phrasing by adjusting parameters like temperature to reduce randomness.

Finally, post-generation refinement involves automated or manual checks to fix inconsistencies. Tools like grammar checkers or custom rules (e.g., flagging mixed metaphors or abrupt tone shifts) can identify issues. For example, a script could detect mismatched units (e.g., “5 miles” vs. “8 kilometers”) and standardize them. Iterative feedback loops, where users rate answer coherence, can further refine the system. If a generated answer mentions “deep learning” early but shifts to “neural networks” without explanation, a post-processor could insert a clarifying phrase like, “often called neural networks.” This step ensures the final output reads as a unified whole, not a patchwork of sources.

By combining these strategies—aligning inputs, guiding generation, and polishing outputs—developers can mitigate the frankenstein effect and produce answers that feel coherent despite diverse source material.