To address the "frankenstein" answer problem in RAG systems—where retrieved passages clash in style or structure—three strategies can improve coherence: passage re-ranking and filtering, context-aware generation, and post-generation refinement. Each approach targets a different stage of the RAG pipeline to ensure the final output is unified and logically consistent.
First, re-ranking or filtering retrieved passages can reduce stylistic conflicts before generation. Instead of relying solely on relevance scores, incorporate metrics that prioritize consistency. For example, cluster passages by writing style (e.g., formal vs. casual) using embeddings, and select a subset from the same cluster. Alternatively, use a cross-encoder model to score passages not just for relevance but also for compatibility with one another. For instance, if two passages contradict in tone (one technical, another conversational), the system could deprioritize one. Tools like Sentence-BERT or LLM-based classifiers can help identify stylistic mismatches during retrieval.
Second, adjust the generation process to synthesize disparate information into a cohesive answer. Explicitly instruct the LLM in the prompt to "merge information into a uniform style" or "write as if explaining to a colleague." For example, a prompt like, "Summarize the following passages in a neutral, professional tone, avoiding abrupt shifts in style," guides the model to harmonize inputs. Additionally, use context window structuring: organize retrieved passages into a logical flow (e.g., chronological order, problem-solution) before feeding them to the generator. This gives the LLM a scaffold to follow, reducing incoherence. For instance, grouping medical study results by date and highlighting key trends before generation can lead to a more narrative output.
Third, post-process the generated answer to fix inconsistencies. Apply a lightweight LLM (e.g., GPT-3.5) to rewrite the output with a focus on style alignment. For example, a follow-up step could ask, "Revise this text to ensure consistent terminology and tone." Alternatively, use rule-based checks: detect abrupt shifts in verb tense, jargon levels, or voice (active/passive) and correct them. Tools like text similarity scores between sentences can flag disjointed sections. For instance, if one sentence uses "users" and another "clients," the system could standardize the term. Iterative refinement loops—where the output is regenerated or edited multiple times—can also smooth out inconsistencies.
By combining these strategies—curating inputs, guiding generation, and refining outputs—developers can mitigate the frankenstein effect and produce answers that feel cohesive despite heterogeneous source material.