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.github/policies/disallow-edits.yml

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permission: Read
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then:
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- closePullRequest
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- description: (This is only a test policy that will be deleted.) Disallow sign-off for articles in the /docs/csharp folder.
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if:
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- or:
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- payloadType: Issue_Comment
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- payloadType: Pull_Request_Review_Comment
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- isAction:
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action: Created
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- isActivitySender:
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issueAuthor: True
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- commentContains:
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pattern: '#sign-off'
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isRegex: False
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- isAssignedToUser:
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user: BillWagner
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- not:
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isActivitySender:
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user: BillWagner
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then:
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# Add the do-not-merge label, remove the ready-to-merge label, and add a reply asking the PR author not to sign off on the PR.
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- addReply:
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reply: >-
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@${issueAuthor} - Please do NOT sign off on this pull request!!
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- addLabel:
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label: do-not-merge
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- removeLabel:
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label: ready-to-merge

docs/ai/azure-ai-for-dotnet-developers.md

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title: Develop .NET apps that use Azure AI services
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description: This article provides an organized list of resources about Azure AI scenarios for .NET developers, including documentation and code samples.
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ms.date: 12/19/2024
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ms.date: 05/29/2025
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ms.topic: overview
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ms.custom: devx-track-dotnet, devx-track-dotnet-ai
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---

docs/ai/conceptual/chain-of-thought-prompting.md

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title: "Chain of Thought Prompting - .NET"
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description: "Learn how chain of thought prompting can simplify prompt engineering."
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author: catbutler
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ms.topic: concept-article #Don't change.
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ms.date: 11/24/2024
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ms.date: 05/29/2025
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#customer intent: As a .NET developer, I want to understand what chain-of-thought prompting is and how it can help me save time and get better completions out of prompt engineering.
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docs/ai/conceptual/embeddings.md

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title: "How Embeddings Extend Your AI Model's Reach"
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description: "Learn how embeddings extend the limits and capabilities of AI models in .NET."
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author: catbutler
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ms.topic: concept-article #Don't change.
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ms.date: 12/19/2024
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ms.date: 05/29/2025
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#customer intent: As a .NET developer, I want to understand how embeddings extend LLM limits and capabilities in .NET so that I have more semantic context and better outcomes for my AI apps.
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docs/ai/conceptual/how-genai-and-llms-work.md

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title: "How Generative AI and LLMs work"
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description: "Understand how Generative AI and large language models (LLMs) work and how they might be useful in your .NET projects."
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author: haywoodsloan
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ms.topic: concept-article
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ms.date: 11/24/2024
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ms.date: 05/29/2025
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#customer intent: As a .NET developer, I want to understand how Generative AI and large language models (LLMs) work and how they may be useful in my .NET projects.
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docs/ai/conceptual/prompt-engineering-dotnet.md

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title: Prompt Engineering concepts
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description: Learn basic prompt engineering concepts and how to implement them using .NET tools such as the Semantic Kernel SDK
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author: catbutler
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ms.topic: concept-article #Don't change.
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ms.date: 04/09/2025
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docs/ai/conceptual/rag.md

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title: "Integrate Your Data into AI Apps with Retrieval-Augmented Generation"
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description: "Learn how retrieval-augmented generation lets you use your data with LLMs to generate better completions in .NET."
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author: catbutler
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ms.topic: concept-article #Don't change.
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ms.date: 11/24/2024
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ms.date: 05/29/2025
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#customer intent: As a .NET developer, I want to understand how retrieval-augmented generation works in .NET so that LLMs can use my data sources to provide more valuable completions.
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docs/ai/conceptual/understanding-openai-functions.md

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title: "Understanding OpenAI Function Calling"
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description: "Understand how function calling enables you to integrate external tools with your OpenAI application."
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author: haywoodsloan
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ms.topic: concept-article
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ms.date: 12/19/2024
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#customer intent: As a .NET developer, I want to understand OpenAI function calling so that I can integrate external tools with AI completions in my .NET project.
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docs/ai/conceptual/understanding-tokens.md

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title: "Understanding tokens"
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description: "Understand how large language models (LLMs) use tokens to analyze semantic relationships and generate natural language outputs"
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author: haywoodsloan
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ms.topic: concept-article
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#customer intent: As a .NET developer, I want understand how large language models (LLMs) use tokens so I can add semantic analysis and text generation capabilities to my .NET projects.
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---
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# Understand tokens

docs/ai/conceptual/vector-databases.md

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title: "Using Vector Databases to Extend LLM Capabilities"
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description: "Learn how vector databases extend LLM capabilities by storing and processing embeddings in .NET."
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author: catbutler
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ms.topic: concept-article #Don't change.
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ms.date: 11/24/2024
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#customer intent: As a .NET developer, I want to learn how vector databases store and process embeddings in .NET so I can make more data available to LLMs in my apps.
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