1. Dependency on Sequential Accuracy and Error Propagation The primary challenge in keeping generated outputs grounded with multi-step retrieval is the reliance on sequential accuracy. Each step depends on the correctness of prior retrievals, creating a chain of dependencies. For example, if a system first retrieves an incorrect fact (e.g., misidentifying a historical event’s date), subsequent steps might pull related but irrelevant data (e.g., wrong cultural trends for that period). Without validation at each stage, errors in early steps directly skew later retrievals. This makes the system vulnerable to cascading failures, as even minor inaccuracies in initial steps propagate and distort the final output.
2. Amplification of Errors Through Context Shifts Errors compound as each step amplifies prior mistakes. For instance, in a medical diagnosis pipeline, misretrieving a symptom (e.g., confusing “fatigue” with “fever”) could lead the system to retrieve incorrect conditions (e.g., malaria instead of anemia). Subsequent steps might then suggest inappropriate treatments (e.g., antimalarial drugs). The lack of corrective mechanisms between steps allows errors to snowball, turning a small initial mistake into a critical failure. This is especially problematic in open-domain systems, where retrievals are broad and context shifts (e.g., mixing timelines or topics) can compound misunderstandings.
3. Complexity of Context Management and Validation Multi-step systems struggle to maintain coherent context across retrievals. Each step may introduce new data that conflicts with earlier information. For example, a QA system answering “How did X policy affect economy Y?” might first retrieve outdated GDP figures, then use them to pull irrelevant analysis from a later decade. The model must reconcile these discrepancies, but without explicit checks, conflicting data worsens output quality. Additionally, varying reliability of sources (e.g., mixing peer-reviewed studies and blog posts) introduces noise, making it harder to distinguish valid information. This complexity demands robust validation layers, which are often omitted due to computational or design constraints.