RRepoGEO

REPOGEO REPORT · LITE

dotnet/ai-samples

Default branch main · commit dfffa9b6 · scanned 6/7/2026, 2:41:45 AM

GitHub: 871 stars · 182 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface dotnet/ai-samples, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the repo's 'About' section

    Why:

    COPY-PASTE FIX
    Official .NET samples demonstrating how to integrate and use AI capabilities, including LLMs and embeddings, in your C# applications.
  • mediumreadme#2
    Clarify the README's opening to emphasize its role as a sample repository for *using* AI libraries

    Why:

    CURRENT
    Welcome to the official home for .NET samples demonstrating how to use AI in your .NET applications.
    COPY-PASTE FIX
    Welcome to the official home for .NET samples demonstrating how to integrate and use AI capabilities, including large language models (LLMs) and embeddings, in your C# applications. This repository focuses on practical examples for *using* existing AI libraries and services, not on providing a new AI library.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface dotnet/ai-samples
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/semantic-kernel
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/semantic-kernel · recommended 3×
  2. Azure/azure-sdk-for-net · recommended 2×
  3. okham/openai-dotnet · recommended 1×
  4. Hugging Face Inference API · recommended 1×
  5. microsoft/onnxruntime · recommended 1×
  • CATEGORY QUERY
    How can I integrate large language models into my C# .NET applications?
    you: not recommended
    AI recommended (in order):
    1. Semantic Kernel (microsoft/semantic-kernel)
    2. OpenAI .NET Library (okham/openai-dotnet)
    3. Azure OpenAI Service SDK (Azure/azure-sdk-for-net)
    4. Hugging Face Inference API
    5. ONNX Runtime (microsoft/onnxruntime)
    6. ML.NET (dotnet/machinelearning)

    AI recommended 6 alternatives but never named dotnet/ai-samples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find sample code for building AI-powered features in .NET?
    you: not recommended
    AI recommended (in order):
    1. Microsoft Learn
    2. dotnet/machinelearning-samples (dotnet/machinelearning-samples)
    3. Azure-Samples/cognitive-services-dotnet-sdk-samples (Azure-Samples/cognitive-services-dotnet-sdk-samples)
    4. Azure-Samples/azure-openai-samples (Azure-Samples/azure-openai-samples)
    5. microsoft/semantic-kernel (microsoft/semantic-kernel)
    6. ML.NET Samples
    7. Semantic Kernel (microsoft/semantic-kernel)
    8. Azure SDK for .NET Samples (Azure/azure-sdk-for-net)
    9. C# Corner
    10. CodeProject
    11. Medium

    AI recommended 11 alternatives but never named dotnet/ai-samples. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of dotnet/ai-samples?
    pass
    AI did not name dotnet/ai-samples — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts dotnet/ai-samples in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dotnet/ai-samples explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo dotnet/ai-samples solve, and who is the primary audience?
    pass
    AI named dotnet/ai-samples explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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dotnet/ai-samples — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite