--- title: RAG Was Always a Temporary Workaround. What is Next? description: RAG pipelines are a high-overhead workaround built around a missing primitive: the ability to persist and transfer neural hidden states directly. The full RAG... canonical: https://daily.dev/posts/rag-was-always-a-temporary-workaround-what-is-next--bonbfkuil twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: RAG Was Always a Temporary Workaround. What is Next? | daily.dev og:description: RAG pipelines are a high-overhead workaround built around a missing primitive: the ability to persist and transfer neural hidden states directly. The full RAG... og:url: https://daily.dev/posts/rag-was-always-a-temporary-workaround-what-is-next--bonbfkuil og:image: https://api.daily.dev/og/posts/BONbFkUIl.png og:image:alt: RAG Was Always a Temporary Workaround. What is Next? og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # RAG Was Always a Temporary Workaround. What is Next? **[Towards Data Science](https://daily.dev/sources/tds)** · 8 min read · 1 upvotes · 0 comments ## Summary RAG pipelines are a high-overhead workaround built around a missing primitive: the ability to persist and transfer neural hidden states directly. The full RAG chain — generate text, embed, store vector, retrieve, reconstruct prompt — exists only because models cannot yet natively persist their own state. Larger context windows don't solve the problem either, as they still require a full prefill pass and carry prohibitive bandwidth costs in multi-agent or edge scenarios. A latency breakdown shows RAG adds ~135ms of sequential, blocking overhead — acceptable for chatbots but fatal for robotics, haptics, or real-time control loops. The evolutionary arc of memory (raw files → relational DBs → search indices → embeddings → vector search → latent persistence) suggests vector search will follow prior layers: it won't disappear, but it will stop being the default conversational memory mechanism. The real challenge is that latent states are model-specific and architecturally incompatible across models, making direct state transfer an open research problem. The prediction: RAG becomes an interoperability layer at human-machine or cross-architecture boundaries, not the primary memory mechanism for AI-to-AI communication. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the approximate latency breakdown for a single RAG call from embedding to decoding? An illustrative RAG call latency budget totals roughly 135 milliseconds: token generation 15ms, embedding 12ms, network I/O 8ms, vector search 25ms, reranking 10ms, prompt reconstruction 15ms, and decoding 50ms. These steps are blocking and sequential, so each stage must wait for the previous one, making the total unacceptable for continuous control loops like robotics or self-driving systems. _Engineers weighing RAG against latency budgets can track systems-design analysis like this on daily.dev._ ### Why can't a larger context window replace retrieval-augmented generation for AI agent memory? A larger context window solves capacity but not portability or persistence. Transferring a multi-million-token prompt between agents, edge devices, or pipeline stages is bandwidth-heavy, and the receiving model still must run a full prefill pass over every token to reconstruct the sender's reasoning state, which is not a free operation even on modern hardware. _Anyone architecting multi-agent memory systems can follow this debate over context windows versus persistence on daily.dev._ ### What technical obstacles stand in the way of directly persisting a neural network's hidden state instead of using RAG? Latent representations are model-specific and unstable across architectures, unlike text. Direct state transfer requires matching layer counts, hidden dimensions, attention layouts, and KV-cache formats, aligning precision (e.g., fp16 versus bf16), matching layer normalization and residual scale, and correctly aligning positional or rotary embeddings; getting any wrong produces confidently coherent nonsense. Research approaches like Inductive Latent Context Persistence currently only work with near-identical models on both ends. _Teams exploring latent-state memory beyond vector databases can keep up with this research direction on daily.dev._ ## Similar posts on daily.dev - [RAG Explained Simply with a Real Project](https://daily.dev/posts/rag-explained-simply-with-a-real-project-pgy15jzg7) · freeCodeCamp · 23 upvotes · 1 comments - [Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI](https://daily.dev/posts/is-rag-dead-the-rise-of-context-engineering-and-semantic-layers-for-agentic-ai-ztxinq3ic) · Towards Data Science · 2 upvotes · 0 comments - [How to improve the memory of AI agents](https://daily.dev/posts/how-to-improve-the-memory-of-ai-agents-zmppe2bgj) · InfoWorld · 0 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#rag](https://daily.dev/tags/rag), [#neural-networks](https://daily.dev/tags/neural-networks), [#vector-search](https://daily.dev/tags/vector-search) [View this post on daily.dev](https://daily.dev/posts/rag-was-always-a-temporary-workaround-what-is-next--bonbfkuil) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"RAG Was Always a Temporary Workaround. What is Next?","url":"https://daily.dev/posts/rag-was-always-a-temporary-workaround-what-is-next--bonbfkuil","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/rag-was-always-a-temporary-workaround-what-is-next--bonbfkuil"},"datePublished":"2026-07-10T14:10:27.204Z","dateModified":"2026-09-13T20:52:42.286Z","description":"RAG pipelines are a high-overhead workaround built around a missing primitive: the ability to persist and transfer neural hidden states directly. The full RAG...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/bd1a9ca4c8b17c96c22c9db75e1f6372?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/bd1a9ca4c8b17c96c22c9db75e1f6372?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Towards Data Science","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Towards Data Science","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds","url":"https://daily.dev/sources/tds"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/rag-was-always-a-temporary-workaround-what-is-next--bonbfkuil","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,ai-agents,rag,neural-networks,vector-search","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Towards Data Science","item":"https://daily.dev/sources/tds"},{"@type":"ListItem","position":3,"name":"RAG Was Always a Temporary Workaround. What is Next?"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/rag-was-always-a-temporary-workaround-what-is-next--bonbfkuil#faq","mainEntity":[{"@type":"Question","name":"What is the approximate latency breakdown for a single RAG call from embedding to decoding?","acceptedAnswer":{"@type":"Answer","text":"An illustrative RAG call latency budget totals roughly 135 milliseconds: token generation 15ms, embedding 12ms, network I/O 8ms, vector search 25ms, reranking 10ms, prompt reconstruction 15ms, and decoding 50ms. These steps are blocking and sequential, so each stage must wait for the previous one, making the total unacceptable for continuous control loops like robotics or self-driving systems. Engineers weighing RAG against latency budgets can track systems-design analysis like this on daily.dev."}},{"@type":"Question","name":"Why can't a larger context window replace retrieval-augmented generation for AI agent memory?","acceptedAnswer":{"@type":"Answer","text":"A larger context window solves capacity but not portability or persistence. Transferring a multi-million-token prompt between agents, edge devices, or pipeline stages is bandwidth-heavy, and the receiving model still must run a full prefill pass over every token to reconstruct the sender's reasoning state, which is not a free operation even on modern hardware. Anyone architecting multi-agent memory systems can follow this debate over context windows versus persistence on daily.dev."}},{"@type":"Question","name":"What technical obstacles stand in the way of directly persisting a neural network's hidden state instead of using RAG?","acceptedAnswer":{"@type":"Answer","text":"Latent representations are model-specific and unstable across architectures, unlike text. Direct state transfer requires matching layer counts, hidden dimensions, attention layouts, and KV-cache formats, aligning precision (e.g., fp16 versus bf16), matching layer normalization and residual scale, and correctly aligning positional or rotary embeddings; getting any wrong produces confidently coherent nonsense. Research approaches like Inductive Latent Context Persistence currently only work with near-identical models on both ends. Teams exploring latent-state memory beyond vector databases can keep up with this research direction on daily.dev."}}]} ```