RRepoGEO

REPOGEO REPORT · LITE

DataExpert-io/llm-driven-data-engineering

Default branch main · commit be4dc8ef · scanned 5/28/2026, 9:22:40 PM

GitHub: 1,152 stars · 230 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 DataExpert-io/llm-driven-data-engineering, 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's opening to clearly state it's a course/tutorial

    Why:

    CURRENT
    # LLM-driven Data Engineering
    COPY-PASTE FIX
    # LLM-driven Data Engineering: A Comprehensive Course and Lab Series

This repository serves as a public course and lab series, designed to go over all the LLM-driven data engineering concepts, from foundational principles to practical applications like RAG and SQL generation.
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository, for example, with the MIT License text, to clearly state how others can use, modify, and distribute the content.

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 DataExpert-io/llm-driven-data-engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Prefect
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Prefect · recommended 2×
  2. LangChain · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Hugging Face Transformers Library · recommended 1×
  5. Apache Spark · recommended 1×
  • CATEGORY QUERY
    How can I integrate large language models into my data engineering pipelines?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Apache Spark
    3. Apache Flink
    4. Apache Airflow
    5. Prefect
    6. OpenAI API
    7. Azure OpenAI Service
    8. LangChain
    9. LlamaIndex
    10. MLflow
    11. Ray
    12. Google Cloud Vertex AI
    13. Amazon SageMaker
    14. Microsoft Azure Machine Learning

    AI recommended 14 alternatives but never named DataExpert-io/llm-driven-data-engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for building RAG applications with external data?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Pinecone
    4. Weaviate
    5. Qdrant
    6. Hugging Face Transformers
    7. Sentence-Transformers
    8. Elasticsearch
    9. OpenSearch
    10. Faiss
    11. Annoy
    12. Airflow
    13. Prefect
    14. Dagster

    AI recommended 14 alternatives but never named DataExpert-io/llm-driven-data-engineering. 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 DataExpert-io/llm-driven-data-engineering?
    pass
    AI named DataExpert-io/llm-driven-data-engineering explicitly

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

  • If a team adopts DataExpert-io/llm-driven-data-engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DataExpert-io/llm-driven-data-engineering 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 DataExpert-io/llm-driven-data-engineering solve, and who is the primary audience?
    pass
    AI did not name DataExpert-io/llm-driven-data-engineering — 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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MARKDOWN (README)
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DataExpert-io/llm-driven-data-engineering — 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