🪟 Most production AI uses RAG and long context together
PM Context Windows
(2026 Edition)
4 architecture tradeoffs and 4 PM questions to ask.
Build Context PM Skills — Free →4 Tradeoffs
1.
Long context — simple but expensive and slow
2.
RAG — flexible but quality depends on retrieval
3.
Memory — persistent across sessions but adds complexity
4.
Hybrid — most production systems combine all three
4 PM Questions
1.
How fresh does the context need to be?
2.
Cost per query — what's the budget?
3.
Latency tolerance — sync vs async?
4.
Privacy — what can leave the user's account?
FAQ
Does long context kill RAG?
No. Long-context models still cost more per query and have attention degradation in the middle of the context. RAG remains cheaper and often more accurate for needle-in-haystack queries. Most production AI uses both — RAG for breadth, long context for depth on the retrieved chunks.