@hackproduct9: "Should I use RAG or long context?" is the wrong question. ⚡ You're going to use both. And memory. The three aren't competing — they answer different questions about what goes in the prompt. Here's the honest comparison 👇 🟡 LONG CONTEXT — put it all in Cost: 198k tokens, every single call. Wait: time-to-first-token climbs with input size. 💥 Breaks when: recall sags in the middle of very long inputs. ✅ Reach for it when: the corpus is small, stable, and needed on every call. 🔵 RAG — fetch only what matters Cost: ~4k tokens per call. 10M chunks in, top 8 out. Wait: one extra retrieval hop. 💥 Breaks when: retrieval misses. If it doesn't fetch it, the model cannot know it — and it will answer anyway. ✅ Reach for it when: the corpus is large, or it changes every day. 🟢 MEMORY — remember the person Cost: ~300 tokens per call. Wait: one key lookup. 💥 Breaks when: yesterday's fact is still true as far as it knows. ✅ Reach for it when: the fact is about this person, across sessions. 👀 The one nobody says: memory is the only one of the three that writes back. RAG reads an index someone else built. Long context reads what you pasted. Memory takes the answer and updates the store — that loop is the entire difference, and it's also why memory rots and the other two don't. 🎯 The rule: pick by what changes. Never → long context. Often → RAG. Per person → memory. 🤖 A support bot runs all three at once: their plan (memory) + today's policy (RAG) + this ticket thread (long context). 📸 Screenshot the comparison. Save it before your next architecture argument. Follow @hackproduct — we turn scary AI-engineering concepts into things you can ship. ⚡ . . #RAG #AIengineering #LLM #longcontext #vectordatabase

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Tuesday 11 August 2026 20:22:28 GMT