@hackproduct9: LLM vs RAG vs AI Agent vs Agentic AI — four words people use like they mean the same thing. 🧠 They don't. Each one is the last one plus ONE capability. Stop asking "do we need an agent?" Ask: which capability is actually missing? 1️⃣ LLM (Large Language Model) — the model on its own. Knows only its training data. → Perfect for: drafting, summarising, rewriting. 2️⃣ RAG (Retrieval Augmented Generation) — add retrieval. Answers from YOUR docs, not its memory. → Perfect for: internal wikis, support bots, policy Q&A. 3️⃣ AI Agent — add tools in a loop. It acts, checks the result, acts again until the task is done. → Perfect for: tickets, data fixes, multi-step workflows. 4️⃣ Agentic AI — add coordination. Many agents, one goal. → Perfect for: research pipelines, end-to-end ops that one context window can't hold. 👀 The part nobody says: most products calling themselves "agentic" are step 2 with a nicer UI. That's not a flaw. Step 2 is cheaper, faster and easier to debug. Rule: add one capability at a time, and only when the step before it is the bottleneck. Where teams go wrong: jumping straight to multi-agent for a problem RAG would solve. Now you're debugging handoffs instead of shipping. 📸 Screenshot the last frame: model + retrieval + tools + coordination. Save it for the next time someone says "we need an agent". Follow @hackproduct — we turn scary AI-engineering concepts into things you can actually ship. ⚡ . . #AIengineering #machinelearning #softwareengineering #LLM #RAG

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Sunday 04 October 2026 00:34:13 GMT