RRepoGEO

REPOGEO REPORT · LITE

athina-ai/rag-cookbooks

Default branch main · commit ab087e8f · scanned 6/25/2026, 5:48:18 AM

GitHub: 2,539 stars · 321 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
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 athina-ai/rag-cookbooks, 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 intro to clarify its role as a practical guide complementing frameworks

    Why:

    CURRENT
    Welcome to the comprehensive collection of advanced + agentic Retrieval-Augmented Generation (RAG) techniques.
    COPY-PASTE FIX
    Welcome to the definitive collection of advanced + agentic Retrieval-Augmented Generation (RAG) *recipes and practical implementations*. This repository provides concrete, ready-to-use solutions and best practices, designed to *complement* popular RAG frameworks like LangChain and LlamaIndex, helping you master specific RAG challenges and build robust systems.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://athina.ai/rag-cookbooks
  • mediumreadme#3
    Add a 'How this compares' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'How RAG Cookbooks Complements Your Stack' or 'RAG Cookbooks vs. Frameworks' that explains how this repository provides practical recipes and solutions that work *with* frameworks like LangChain and LlamaIndex, rather than being a competing framework itself. Highlight that it focuses on specific advanced techniques and their evaluation.

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 athina-ai/rag-cookbooks
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Haystack · recommended 2×
  4. RAGatouille · recommended 1×
  5. Weaviate · recommended 1×
  • CATEGORY QUERY
    How can I implement advanced retrieval augmented generation techniques for better LLM responses?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. RAGatouille
    5. Weaviate
    6. Pinecone
    7. Cohere Rerank API

    AI recommended 7 alternatives but never named athina-ai/rag-cookbooks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical examples and tutorials for building robust agentic RAG systems.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. AutoGen
    5. OpenAI Cookbook
    6. Hugging Face Transformers & Datasets

    AI recommended 6 alternatives but never named athina-ai/rag-cookbooks. 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 athina-ai/rag-cookbooks?
    pass
    AI did not name athina-ai/rag-cookbooks — 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 athina-ai/rag-cookbooks in production, what risks or prerequisites should they evaluate first?
    pass
    AI named athina-ai/rag-cookbooks 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 athina-ai/rag-cookbooks solve, and who is the primary audience?
    pass
    AI named athina-ai/rag-cookbooks explicitly

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

Embed your GEO score

Drop this badge into the README of athina-ai/rag-cookbooks. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/athina-ai/rag-cookbooks.svg)](https://repogeo.com/en/r/athina-ai/rag-cookbooks)
HTML
<a href="https://repogeo.com/en/r/athina-ai/rag-cookbooks"><img src="https://repogeo.com/badge/athina-ai/rag-cookbooks.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

athina-ai/rag-cookbooks — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite