RRepoGEO

REPOGEO REPORT · LITE

towardsai/ragbook-notebooks

Default branch main · commit d2d71659 · scanned 6/1/2026, 1:12:58 PM

GitHub: 550 stars · 200 forks

AI VISIBILITY SCORE
28 /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
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 towardsai/ragbook-notebooks, 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 and introductory paragraph

    Why:

    CURRENT
    # ragbook-notebooks
This is a repository gathering all the notebooks for the Towards AI RAG book.
    COPY-PASTE FIX
    # ragbook-notebooks: Companion Notebooks for "Building LLMs for Production"
This repository provides all the practical, runnable notebooks accompanying the "Building LLMs for Production" book by Towards AI. It serves as a hands-on learning resource for implementing Retrieval Augmented Generation (RAG) systems and advanced LLM applications, rather than a standalone library or framework.
  • mediumlicense#2
    Add a LICENSE file to the repository root

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository to clearly state the terms under which the content is distributed.
  • mediumtopics#3
    Expand repository topics to include educational keywords

    Why:

    CURRENT
    agent, agents, ai, langchain, llamaindex, llm, llms, python, rag
    COPY-PASTE FIX
    agent, agents, ai, langchain, llamaindex, llm, llms, python, rag, llm-education, rag-tutorial, ai-learning, book-companion

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 towardsai/ragbook-notebooks
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Hugging Face Transformers · recommended 2×
  4. OpenAI API · recommended 2×
  5. Hugging Face Datasets · recommended 1×
  • CATEGORY QUERY
    How to build robust retrieval-augmented generation systems for production LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. Hugging Face Datasets
    5. Pinecone
    6. Weaviate
    7. Milvus
    8. Chroma
    9. Qdrant
    10. OpenAI API
    11. Azure OpenAI Service
    12. Anthropic API
    13. Google Gemini API
    14. Elasticsearch
    15. OpenSearch
    16. MLflow
    17. Weights & Biases

    AI recommended 17 alternatives but never named towardsai/ragbook-notebooks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Guidance on developing autonomous AI agents for complex information analysis tasks.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. CrewAI
    5. Haystack
    6. OpenAI API
    7. Hugging Face Transformers

    AI recommended 7 alternatives but never named towardsai/ragbook-notebooks. 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 towardsai/ragbook-notebooks?
    pass
    AI did not name towardsai/ragbook-notebooks — 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 towardsai/ragbook-notebooks in production, what risks or prerequisites should they evaluate first?
    pass
    AI named towardsai/ragbook-notebooks 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 towardsai/ragbook-notebooks solve, and who is the primary audience?
    pass
    AI named towardsai/ragbook-notebooks explicitly

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

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