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The Context Window Dilemma: When Bigger Context Windows Help, and When They Don’t

1 min read
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Opinions on LLMs Context Window

Bigger context windows are the hype, but the payoff isn’t universal. A lively thread on LocalLLaMA asks whether context length changes outcomes for unrelated questions, with oss-gtp:20b used as the baseline in a hypothetical, session-isolated setup [1].

Context length reality: when it helps vs when it doesn’t — The discussion suggests that longer windows don’t automatically improve every short, recall-based prompt. New sessions stay isolated, so extra history mainly matters if the user expects continuity across turns [1].

Memorization geometry — A separate post highlights that deep sequence models tend to memorize in geometric ways, reminding us memory layout matters even before tool choice, and that how a prompt is structured can affect what sticks [2].

Long documents in practice — For 900+ pages of legal text, chatter points to extreme-context options like Unsloth and Qwen3-VL models with 1M context, but voices caveat speed, cost, and hallucination risk; chunking and careful prompting remain part of the playbook [3].

RAG and up-to-date knowledge — In OpenWebUI-driven workflows, with Chroma or similar retrievers, keeping a knowledge base fresh is fiddly: older versions linger, so teams juggle updates, versioning, and cleanup rather than relying on a bigger window alone [4].

Bottom line: context windows push usefulness in the right scenarios, but memory geometry, processing heft, and retrieval design often decide whether you gain precision or just baggage.

References

[1]
Reddit

Does the context length setting have any relevance on a series of completely unrelated questions?

Discusses whether changing context length affects independent short queries, session isolation, and potential remnants in memory for local LLMs.

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[2]
HackerNews

Deep sequence models tend to memorize geometrically

Paper claims deep sequence models memorize information geometrically; implications for LLM training, privacy, generalization, and leakage risks in large systems.

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[3]
Reddit

Best model for processing large legal contexts (900+ pages)

Post discusses model size, context window, and techniques for processing 900+ pages of legal text with minimal hallucinations in practice.

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[4]
Reddit

Specific RAG use, what would you do?

Discusses auto-updating knowledge base, deleting older versions, and improving RAG workflows with OpenWebUI or alternatives

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