{"schema_version":"onlylabs.public_signal.v1","title":"Scaleway Writing: Your RAG-powered AI app in 50 lines of code!","description":"Scaleway writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/13e0022f-0954-411e-83a6-f8aeec899e1f","json_url":"https://onlylabs.fyi/signals/13e0022f-0954-411e-83a6-f8aeec899e1f/signal.json","generated_at":"2026-06-07T21:16:32.606045+00:00","org":{"slug":"scaleway","name":"Scaleway","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/scaleway","dossier_json_url":"https://onlylabs.fyi/labs/scaleway/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/13e0022f-0954-411e-83a6-f8aeec899e1f","signal_json":"https://onlylabs.fyi/signals/13e0022f-0954-411e-83a6-f8aeec899e1f/signal.json","source":"https://www.scaleway.com/en/blog/your-rag-powered-ai-app-in-50-lines-of-code/","lab_dossier":"https://onlylabs.fyi/labs/scaleway","lab_dossier_json":"https://onlylabs.fyi/labs/scaleway/dossier.json","analysis":"https://onlylabs.fyi/analysis/scaleway","analysis_json":"https://onlylabs.fyi/analysis/scaleway/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/scaleway/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"Scaleway published Your RAG-powered AI app in 50 lines of code!. 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Build • Diego Coy • 09/04/24 • 14 min read Introduction This article continues the journey we embarked on a few weeks back with our last practical AI blog post: “ Ollama: from zero to running an LLM in less than 2 minutes! ” where we leveraged Ollama to procure and serve an LLM in a virtual machine equipped with a GPU, Scaleway's H100 PCIe GPU Instance . After going through that article you may have been inspired to integrate AI capabilities into your own applications (Did you? Let me know via the Scaleway Community !) and you may have realized that even though thousands of possibilities opened up for you, there may still be some scenarios missing in the picture, such as the ability to make an LLM interact with your data. This is where RAG, the focus of this article, comes in. The term RAG stands for Retrieval-augmented generation, which is a technique that augments the usefulness of an LLM by enabling it to generate responses based on an extended set of information you provide. 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Build • Diego Coy • 09/04/24 • 14 min read Introduction This article continues the journey we embarked on a few weeks back with our last practical AI blog post: “ Ollama: from zero to running an LLM in less than 2 minutes! ” where we leveraged Ollama to procure and serve an LLM in a virtual machine equipped with a GPU, Scaleway's H100 PCIe GPU Instance . After going through that article you may have been inspired to integrate AI capabilities into your own applications (Did you? Let me know via the Scaleway Community !) and you may have realized that even though thousands of possibilities opened up for you, there may still be some scenarios missing in the picture, such as the ability to make an LLM interact with your data. This is where RAG, the focus of this article, comes in. The term RAG stands for Retrieval-augmented generation, which is a technique that augments the usefulness of an LLM by enabling it to generate responses based on an extended set of information you provide. 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