RRepoGEO

REPOGEO REPORT · LITE

aws-samples/aws-genai-llm-chatbot

Default branch main · commit c0be107a · scanned 6/26/2026, 6:16:57 PM

GitHub: 1,401 stars · 435 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)

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

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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/aws-genai-llm-chatbot, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 to emphasize 'AWS deployment solution'

    Why:

    CURRENT
    # AWS GenAI LLM Chatbot

Enterprise-ready generative AI chatbot with RAG capabilities.
    COPY-PASTE FIX
    # AWS GenAI LLM Chatbot: An Enterprise-Ready Reference Architecture for Multi-LLM and Multi-RAG Chatbots on AWS
  • mediumtopics#2
    Refine topics to emphasize 'AWS solution' and remove misleading framework tags

    Why:

    CURRENT
    amazon-bedrock, aurora, aws, bedrock, cdk, chatbot, claude, genai, huggingface, idefics, kendra, langchain, llm, opensearch, opensearch-serverless, pgvector, sagemaker, semantic-search, vectordb
    COPY-PASTE FIX
    amazon-bedrock, aws, bedrock, cdk, chatbot, genai, llm, rag, reference-architecture, solution-blueprint, enterprise-solution, multi-llm, multi-rag, sagemaker, opensearch, pgvector, aurora, kendra, claude, huggingface, idefics, semantic-search, vectordb
  • mediumabout#3
    Clarify the 'About' description to highlight its nature as an AWS deployment blueprint

    Why:

    CURRENT
    A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS
    COPY-PASTE FIX
    A modular and comprehensive AWS reference architecture and deployment blueprint for an enterprise-grade Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK.

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/aws-genai-llm-chatbot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Azure OpenAI Service
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure OpenAI Service · recommended 2×
  2. Hugging Face Inference Endpoints · recommended 2×
  3. elastic/elasticsearch · recommended 2×
  4. langchain-ai/langchain · recommended 2×
  5. run-llama/llama_index · recommended 2×
  • CATEGORY QUERY
    How to build a secure enterprise chatbot with RAG capabilities and diverse model support?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Studio
    2. Azure OpenAI Service
    3. Azure Machine Learning
    4. Azure Cognitive Search
    5. Azure Cosmos DB
    6. AWS Bedrock
    7. Amazon SageMaker
    8. Amazon OpenSearch Service
    9. Google Cloud Vertex AI
    10. Google Cloud Search
    11. AlloyDB Omni
    12. PostgreSQL
    13. Hugging Face Enterprise Hub
    14. Hugging Face Inference Endpoints
    15. Elastic Stack
    16. Elasticsearch (elastic/elasticsearch)
    17. Kibana (elastic/kibana)
    18. LangChain (langchain-ai/langchain)
    19. LlamaIndex (run-llama/llama_index)
    20. Weaviate (weaviate/weaviate)
    21. Pinecone
    22. Qdrant (qdrant/qdrant)
    23. Cohere Platform
    24. OpenAI API

    AI recommended 24 alternatives but never named aws-samples/aws-genai-llm-chatbot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a solution to deploy a scalable chatbot with RAG and multiple language model integrations.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. FastAPI (tiangolo/fastapi)
    3. Kubernetes (kubernetes/kubernetes)
    4. LlamaIndex (run-llama/llama_index)
    5. Flask (pallets/flask)
    6. Django (django/django)
    7. Docker Swarm (moby/moby)
    8. AWS ECS
    9. Hugging Face Transformers (huggingface/transformers)
    10. Hugging Face Inference Endpoints
    11. Gradio (gradio-app/gradio)
    12. Streamlit (streamlit/streamlit)
    13. Microsoft Azure Bot Service
    14. Azure AI Search
    15. Azure OpenAI Service
    16. Google Cloud Dialogflow CX
    17. Vertex AI Search
    18. Vertex AI PaLM
    19. Vertex AI Gemini
    20. AWS Lex
    21. Amazon Kendra
    22. Amazon Bedrock
    23. Node.js (nodejs/node)
    24. Express.js (expressjs/express)
    25. Elasticsearch (elastic/elasticsearch)
    26. Pinecone
    27. OpenAI API
    28. Anthropic API

    AI recommended 28 alternatives but never named aws-samples/aws-genai-llm-chatbot. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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