When the integration between retrieval and generation in a system like a retrieval-augmented language model (RAG) is poorly tuned, three primary failure modes emerge. First, the generator may ignore retrieved content, relying instead on its internal knowledge or biases. This happens when the retriever provides low-quality or irrelevant documents, or when the generator isn’t trained to prioritize retrieved information. For example, if a user asks about a recent software library update, but the retriever fails to fetch the latest documentation, the generator might default to outdated information stored in its training data. This leads to incorrect or stale answers, even when up-to-date sources exist. Without explicit training to "trust" retrieved content, the generator may also hallucinate plausible-sounding but factually wrong responses, especially in domains where its internal knowledge is weak.
Second, misassociation occurs when the generator incorrectly links retrieved documents to the query. This often stems from poor alignment between the retriever’s output and the generator’s attention mechanisms. For instance, in a technical support scenario, if the retriever provides documents about error messages from two different systems, the generator might conflate troubleshooting steps, suggesting irrelevant fixes. This is exacerbated when documents share overlapping keywords but differ in context. A model might see "Python thread crash" in both a web framework error log and a data science tool’s documentation, then incorrectly recommend solutions from the wrong domain. Fine-grained mechanisms to weigh document relevance (e.g., metadata or confidence scores) are often missing in basic RAG setups, increasing this risk.
Third, partial or fragmented use of retrieved information leads to incomplete answers. The generator might focus on a single document while ignoring complementary information from others. For example, when answering a question about cloud service pricing, the model might cite compute costs from one document but overlook network egress fees mentioned in another, giving an inaccurate total estimate. This occurs when the generator lacks training to synthesize multiple sources or when retrieval returns redundant or conflicting data. Without explicit prompts to compare or aggregate information, the model defaults to surface-level patterns, missing critical nuances. Developers often underestimate the need for post-retrieval processing (e.g., reranking, summarization) to mitigate this.