Practical Implications: Long Context vs. RAG, and Real Model Limits
~13 min read
A large context window doesn't mean you should always use it — cost, latency, and the 'lost in the middle' problem shape when long context genuinely helps versus when RAG is still the better tool.
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Key points
- •Real model limits: Claude up to 200k tokens, Gemini up to 1M tokens, GPT-4o 128k tokens — genuinely large, usable capacities, not just marketing numbers
- •Bigger context isn't automatically better: cost and latency scale with input size, directly following from the O(n^2) attention cost covered earlier
- •'Lost in the middle': models recall information at the start/end of a long context more reliably than information buried in the middle
- •Long-context conversations reprocess the full history every turn unless prefix caching (from llm-optimization) is used by the serving layer
- •Practical rule: use long context when a task genuinely needs whole-document reasoning; use RAG when a large knowledge base only needs a small relevant slice per query