I'm building a knowledge management system across multiple LLM environments and documenting the failure modes that show up in sustained, multi-session work.
RAG is probably where this shows up most clearly — the retrieval step is the first environment boundary, and it runs before the model ever touches the question. The context window that gets assembled determines which relationships are visible, which analogies are available, which answer shapes get primed. You can have the same underlying model, the same query, and structurally different responses depending purely on what the retrieval layer surfaces.
The tool-based case is slightly different — it's less about priming and more about the confidence-masking effect. When a tool returns results, the model tends to stop looking. The environment closes the search early. Both are worth testing for explicitly rather than assuming away.
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RAG is probably where this shows up most clearly — the retrieval step is the first environment boundary, and it runs before the model ever touches the question. The context window that gets assembled determines which relationships are visible, which analogies are available, which answer shapes get primed. You can have the same underlying model, the same query, and structurally different responses depending purely on what the retrieval layer surfaces.
The tool-based case is slightly different — it's less about priming and more about the confidence-masking effect. When a tool returns results, the model tends to stop looking. The environment closes the search early. Both are worth testing for explicitly rather than assuming away.