Retrieving a larger number of documents (e.g., top-10 or top-20) as context for an LLM can improve the model’s ability to synthesize comprehensive answers, especially for complex or ambiguous queries. For example, a question like “What factors contributed to the fall of the Roman Empire?” might require insights from multiple sources covering political, economic, and social angles. Including more documents increases the likelihood of capturing diverse perspectives, redundant information (which can help mitigate errors in individual sources), and niche details that lower-ranked documents might provide. This approach is also useful when the retrieval system isn’t perfectly accurate, as expanding the context pool compensates for potential ranking flaws. However, it comes with trade-offs, such as higher computational costs and the risk of overwhelming the model with irrelevant data.
The primary disadvantage of retrieving many documents is the increased noise and reduced focus. LLMs have limited context windows, so adding more content forces the model to prioritize which information to use, often leading to distractions from lower-relevance documents. For instance, in a coding question like “How to implement authentication in Python,” top-20 results might include outdated libraries or tangential discussions, diluting the answer’s quality. Longer contexts also raise API costs and latency, especially with token-based pricing. Additionally, irrelevant details in the context can cause the model to generate speculative or conflicting answers, as seen in cases where documents disagree on facts or best practices. This trade-off becomes critical in real-time applications where speed and cost efficiency matter.
In contrast, limiting context to top-3 documents ensures tighter focus and efficiency. This works well for straightforward queries, such as “syntax for Python list comprehensions,” where precision and brevity are key. Smaller contexts reduce noise, lower costs, and fit within tighter token limits, making them practical for scalable systems. However, the risk lies in missing critical information. For example, a medical query might require balancing mainstream treatments (top-3) with lesser-known alternatives (top-10). The choice depends on the use case: prioritize depth and coverage for exploratory tasks, but lean toward brevity for well-defined problems where speed and cost are constraints. Testing both approaches with real-world data is often necessary to find the right balance.