We hear agents everywhere. But are they truly REAL AGENTS? Most “agents” we hear about today aren’t agents at all—they’re workflows! A few lines of code calling an LLM, some tools, and voilà—a response. This is not an agent. Here's what it is…
@whats_ai
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CAG vs RAG vs Hybrid: Choosing the Right Data Approach
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So, Which One Should You Use?
• CAG: Perfect for smaller, static datasets where speed and simplicity matter most.
• RAG: The go-to for large, dynamic datasets where real-time retrieval is a must.
• Hybrid: Why not combine both? Use CAG for frequent queries and RAG for edge -
Context-Augmented Generation: Memory and Scalability Limitations
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That said, CAG has downsides:
• You need lots of memory as you fill the context window at every query.
• You cannot upload your whole million-rows dataset. It must be smaller than the current model's context window (e.g. 128k, 200k… tokens).
• You must host the model -
CAG Approach Eliminates Retrieval Latency in AI Systems
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Why is this CAG thing exciting:
• There's no retrieval latency. It just answers much faster.
• It's much simpler, with no complex retrieval pipelines or anything to build, really.
• Avoids retrieval-related mistakes (obviously, since there's no retrieval) -
CAG Preloads Knowledge into Long-Context Model Memory
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On the other hand, CAG preloads all the necessary knowledge into a long-context model’s memory in its KV cache. A complicated way of saying the database here is pre-computed into the model's initial state.
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RAG vs CAG: Understanding Two Augmented Generation Approaches
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So you are wondering between these two options: Retrieval-Augmented Generation (RAG) Cache-Augmented Generation (CAG) What are those?
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CAG vs RAG: New Approaches to Reduce AI Hallucinations
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AI keeps evolving, and so do the ways we integrate knowledge into language models. CAG (Cache-Augmented Generation) is the new "hot sauce" for tackling hallucination and speeding up AI responses. But where does it fit alongside RAG (Retrieval-Augmented Generation)?
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CAG versus RAG: Which AI approach do you need?
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CAG, RAG, BAG, MAG, GAG… Okay, seriously, CAG or RAG? Which one do you need?
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Choosing the Right AI Model and UI for Development Efficiency
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It’s not about always using the most advanced model. Some, like o1, can be phenomenal but are overkill for most cases. It’s about finding the right model and UI for the job. Cursor’s integrated workflow or 4o with Canvas, for instance, has been a game-changer for efficiency.
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AI Models Selection Guide: Tasks and Tools Recommendations
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4o for quick tasks Claude 3.5 Sonnet for conversation-related (human voice) tasks Cursor and 4o for coding o1 for tackling complex, multi-step tasks 4o + Canvas for text editing Google search with Gemini + Perplexity for internet and fact-related queries