https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/tags/rag/Recent content in Rag on TCS Labs AcademyHugoenhttps://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-engineering-foundations-for-platform-engineers/retrieval-and-context-for-infrastructure/rag/Mon, 01 Jan 0001 00:00:00 +0000https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-engineering-foundations-for-platform-engineers/retrieval-and-context-for-infrastructure/rag/https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-engineering-foundations-for-platform-engineers/retrieval-and-context-for-infrastructure/Mon, 01 Jan 0001 00:00:00 +0000https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-engineering-foundations-for-platform-engineers/retrieval-and-context-for-infrastructure/<p>An agent that can only reason over its training data is an agent that will confidently describe a cluster that no longer exists. The training cutoff is months or years in the past; your cluster changes daily. Retrieval-augmented generation (RAG) is the bridge between a model&rsquo;s frozen weights and the live state of your infrastructure.</p> <p>This course teaches you to think about context as a resource to be managed. You will learn why context windows are finite and why that constraint shapes every design decision in an agent-powered ops workflow. You will see exactly which sources of truth - <code>kubectl</code>, <code>mesheryctl</code>, MeshSync, and Meshery designs - contain the state an agent needs, and how to surface that state at query time rather than baking it into a prompt upfront.</p>