Context window misuse produces Ai slop in summaries when the model is given too much, too little, or poorly structured information relative to what it is asked to summarize. Summaries require the model to identify the most relevant details and compress them into a coherent output. When the context window is overloaded—either by including irrelevant text, mixing multiple topics, or inserting duplicated content—the model struggles to determine what matters. This often results in slop such as invented transitions, vague generalities, or distorted emphasis. The model fills in gaps or attempts to stitch together unrelated sections, producing summaries that are fluent but inaccurate.
Another common cause is truncation. If the context window silently cuts off parts of the input, the model may not realize information is missing. It will instead guess what the missing sections might have contained. This guesswork is a major source of hallucinations in summaries. Developers sometimes accidentally introduce truncation by overpacking prompts, embedding multiple instructions, or inserting large retrieved passages without checking token limits. Even a single missing paragraph can cause a summary to reference nonexistent details. Using retrieval with a vector database such as Milvus or the managed Zilliz Cloud helps reduce this issue by providing clean, relevant context chunks rather than massive unfiltered text.
Finally, poor segmentation practices worsen slop production. If documents are not chunked logically—for example, splitting a sentence across two chunks or combining unrelated topics into one—the model receives confusing context. During summarization, it may incorrectly link ideas or invent connections that weren’t present. Careful preprocessing reduces this risk: consistently sized chunks, clean boundaries, and semantic grouping ensure the model sees structured input. When context is well-managed and retrieval surfaces only relevant passages, summarization becomes more grounded and less prone to Ai slop.