<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Prompting on TCS Labs Academy</title><link>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/tags/prompting/</link><description>Recent content in Prompting on TCS Labs Academy</description><generator>Hugo</generator><language>en</language><atom:link href="https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/tags/prompting/index.xml" rel="self" type="application/rss+xml"/><item><title>Prompting</title><link>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-engineering-foundations-for-platform-engineers/prompt-engineering-for-cloud-native-operations/prompting/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-engineering-foundations-for-platform-engineers/prompt-engineering-for-cloud-native-operations/prompting/</guid><description/></item><item><title>2. Prompt Engineering for Cloud Native Operations</title><link>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-engineering-foundations-for-platform-engineers/prompt-engineering-for-cloud-native-operations/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-engineering-foundations-for-platform-engineers/prompt-engineering-for-cloud-native-operations/</guid><description>&lt;p&gt;A prompt is not a search query. When you attach an LLM to a live Kubernetes cluster through Meshery, the quality of your prompts determines whether the agent converges on a safe, correct action or drifts into hallucinated resource names and fabricated flags. This course treats prompt engineering as an operational discipline with the same rigor you would apply to any other systems interface.&lt;/p&gt;
&lt;p&gt;You will move through five tightly scoped lessons: defining the role split between system and user instructions, grounding every prompt in real cluster state from &lt;code&gt;kubectl&lt;/code&gt; or MeshSync, coercing the model into machine-readable YAML and JSON, reusing proven prompt patterns for common ops tasks, and building a small eval set so you catch regressions before they reach production.&lt;/p&gt;</description></item></channel></rss>