RRepoGEO

REPOGEO REPORT · LITE

aws-samples/amazon-bedrock-workshop

Default branch main · commit 8b378f27 · scanned 5/20/2026, 7:48:00 PM

GitHub: 2,148 stars · 930 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 aws-samples/amazon-bedrock-workshop, 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
  • highreadme#1
    Reposition the README opening to emphasize its nature as a guided learning path

    Why:

    CURRENT
    This hands-on workshop, aimed at developers and solution builders, introduces how to leverage foundation models (FMs) through Amazon Bedrock.
    COPY-PASTE FIX
    This repository offers a comprehensive, hands-on workshop designed as a guided learning path for developers and solution builders to get started with and leverage foundation models (FMs) through Amazon Bedrock.
  • mediumabout#2
    Enhance the About description to reinforce the workshop's learning focus

    Why:

    CURRENT
    This is a workshop designed for Amazon Bedrock a foundational model service.
    COPY-PASTE FIX
    A hands-on workshop providing a guided learning path for developers and solution builders to build generative AI applications with Amazon Bedrock.

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 aws-samples/amazon-bedrock-workshop
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. Hugging Face Transformers Library · recommended 1×
  3. LangChain · recommended 1×
  4. Google Cloud Vertex AI · recommended 1×
  5. Anthropic Claude API · recommended 1×
  • CATEGORY QUERY
    How to get started building generative AI applications using large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Hugging Face Transformers Library
    3. LangChain
    4. Google Cloud Vertex AI
    5. Anthropic Claude API
    6. Llama 2
    7. Microsoft Azure OpenAI Service

    AI recommended 7 alternatives but never named aws-samples/amazon-bedrock-workshop. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are best practices for implementing retrieval-augmented generation (RAG) with foundation models?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (weaviate/weaviate)
    3. Qdrant (qdrant/qdrant)
    4. Faiss (facebookresearch/faiss)
    5. Elasticsearch (elastic/elasticsearch)
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)
    8. Sentence Transformers (UKPLab/sentence-transformers)
    9. OpenAI Embeddings
    10. Hugging Face (huggingface/transformers)
    11. Cohere Embeddings
    12. Pyserini (castorini/pyserini)
    13. RAGAS (explodinggradients/ragas)

    AI recommended 13 alternatives but never named aws-samples/amazon-bedrock-workshop. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 aws-samples/amazon-bedrock-workshop?
    pass
    AI did not name aws-samples/amazon-bedrock-workshop — 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 aws-samples/amazon-bedrock-workshop in production, what risks or prerequisites should they evaluate first?
    pass
    AI named aws-samples/amazon-bedrock-workshop 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 aws-samples/amazon-bedrock-workshop solve, and who is the primary audience?
    pass
    AI did not name aws-samples/amazon-bedrock-workshop — 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?

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