RRepoGEO

REPOGEO REPORT · LITE

databricks/lilac

Default branch main · commit b7d92b77 · scanned 5/27/2026, 11:36:44 PM

GitHub: 1,071 stars · 105 forks

AI VISIBILITY SCORE
40 /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
3 / 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 databricks/lilac, 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 H3 to specify category

    Why:

    CURRENT
    <h3 align="center" style="font-size: 20px; margin-bottom: 4px">Better data, better AI</h3>
    COPY-PASTE FIX
    <h3 align="center" style="font-size: 20px; margin-bottom: 4px">The Open-Source Platform for LLM Data Curation and Quality Control</h3>
  • hightopics#2
    Add specific LLM and data curation topics

    Why:

    CURRENT
    artificial-intelligence, data-analysis, dataset-analysis, unstructured-data
    COPY-PASTE FIX
    artificial-intelligence, data-analysis, dataset-analysis, unstructured-data, llm, large-language-models, data-curation, data-quality, nlp-datasets, text-processing
  • mediumcomparison#3
    Add a "Comparison to Alternatives" section in README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

(Add a section here comparing Lilac to tools like Argilla, Snorkel Flow, Label Studio, Prodigy, and Cleanlab Studio, highlighting Lilac's unique strengths in LLM data curation, on-device processing, and UI/Python API.)

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 databricks/lilac
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. Prodigy · recommended 2×
  3. pandas-dev/pandas · recommended 1×
  4. nltk/nltk · recommended 1×
  5. explosion/spaCy · recommended 1×
  • CATEGORY QUERY
    How can I improve the quality of my datasets for training large language models?
    you: not recommended
    AI recommended (in order):
    1. Pandas (pandas-dev/pandas)
    2. NLTK (Natural Language Toolkit) (nltk/nltk)
    3. SpaCy (explosion/spaCy)
    4. OpenRefine (OpenRefine/OpenRefine)
    5. Hugging Face Datasets library (huggingface/datasets)
    6. NLPAug (makcedward/nlpaug)
    7. TextAttack (TextAttack/TextAttack)
    8. Hugging Face Transformers (huggingface/transformers)
    9. Prodigy
    10. Label Studio (HumanSignal/label-studio)
    11. Amazon Mechanical Turk
    12. Scale AI
    13. modAL (cosmo-ethz/modAL)
    14. Lightly (lightly-ai/lightly)
    15. GPT-3/GPT-4
    16. Hugging Face Transformers (huggingface/transformers)
    17. DVC (Data Version Control) (iterative/dvc)
    18. MLflow (mlflow/mlflow)
    19. Git LFS (Large File Storage) (git-lfs/git-lfs)

    AI recommended 19 alternatives but never named databricks/lilac. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help analyze and curate unstructured text data for LLM fine-tuning?
    you: not recommended
    AI recommended (in order):
    1. Argilla
    2. Snorkel Flow
    3. Label Studio
    4. Prodigy
    5. Cleanlab Studio
    6. Weights & Biases
    7. OpenRefine

    AI recommended 7 alternatives but never named databricks/lilac. 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 databricks/lilac?
    pass
    AI named databricks/lilac explicitly

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

  • If a team adopts databricks/lilac in production, what risks or prerequisites should they evaluate first?
    pass
    AI named databricks/lilac 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 databricks/lilac solve, and who is the primary audience?
    pass
    AI named databricks/lilac explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite