How can the prompt be designed to handle contradictory information in retrieved documents (for example, guiding the model on how to reconcile conflicts)?

To handle contradictory information in retrieved documents, prompts should guide the model to explicitly recognize conflicts, evaluate source reliability, and prioritize logical consistency. The prompt must instruct the model to avoid ignoring discrepancies and instead provide a structured approach to resolving them. For example, a prompt might ask the model to first identify conflicting claims, compare supporting evidence (e.g., publication dates, author expertise, data sources), and then present a reasoned conclusion. This ensures the model doesn’t default to averaging conflicting answers or favoring one source without justification. Specificity is key: prompts could include directives like, “If sources disagree on X, explain the conflict, assess which source is more recent or authoritative, and summarize the most plausible conclusion.”

A well-structured prompt might break the task into steps. First, the model could be instructed to list all relevant claims and their sources. Next, it might compare metadata like publication date, data collection methods, or the reputation of the publishing organization. For instance, if one document states “Product X requires 8GB RAM” (from a 2022 user manual) and another says “Product X needs 16GB RAM” (from a 2023 technical update), the prompt could guide the model to flag the conflict, note the newer source’s relevance, and suggest the 16GB requirement applies to updated versions. The prompt might also include fallback strategies, such as recommending users verify with official documentation if conflicts remain unresolved.

Finally, the prompt should encourage transparency. For example, it could instruct the model to explicitly state when evidence is inconclusive and outline remaining uncertainties. If two medical studies conflict—one claiming “Treatment A reduces symptoms by 50%” (small sample size) and another showing “no significant effect” (larger study)—the model might highlight the larger study’s statistical power while acknowledging the contradiction. Developers can enhance this by adding constraints like, “Do not present conflicting information as equally valid without context.” Testing prompts with synthetic contradictions (e.g., fabricated date mismatches) helps refine their ability to handle edge cases, ensuring outputs remain actionable despite conflicting inputs.