Including multiple retrieved documents in a prompt can improve an LLM's ability to generate accurate answers by providing broader context and reducing reliance on its internal knowledge, which may be outdated or incomplete. For example, if a user asks about a niche technical topic, listing relevant documents (with titles/sources) gives the model direct access to authoritative information. Titles help the LLM quickly assess the relevance of each document—e.g., a document titled "2023 Kubernetes Networking Guide" signals recency and specificity, allowing the model to prioritize it over generic content. This approach also enables the model to cross-reference details, resolve ambiguities, or identify consensus among sources, which is critical for complex queries. However, the effectiveness depends on document quality: irrelevant or conflicting sources may confuse the model.
On the downside, excessive documents can overwhelm the LLM’s token budget, forcing truncation of critical information or diluting focus. For instance, if a prompt includes 10 lengthy articles for a simple question like "What is React?", the model might struggle to identify the core answer amid redundant or tangential details. Poorly structured document lists—such as unordered snippets without clear titles—can also hinder performance. The LLM might latch onto minor details from an early document and ignore more relevant ones later, especially if the list isn’t prioritized. Additionally, including low-quality sources (e.g., forums with outdated code snippets) increases the risk of the model propagating errors, even if other documents are correct.
To optimize results, developers should curate documents by relevance, recency, and reliability before inclusion. For example, ranking sources by confidence scores from a retrieval system and truncating them to key excerpts helps the model focus. Explicitly formatting the list—using separators, numbered items, or headings like "Source 1: [Title]"—improves readability for the LLM. Testing is critical: in some cases, 3-5 high-quality documents yield better results than 10 mediocre ones. If token limits are tight, summarizing documents (e.g., "Document A concludes X, while Document B argues Y") can preserve context without overwhelming the prompt. Balancing specificity and brevity ensures the model leverages external knowledge without losing coherence.