RRepoGEO

REPOGEO REPORT · LITE

databricks-academy/large-language-models

Default branch published · commit 08a6ae43 · scanned 6/4/2026, 1:32:25 PM

GitHub: 824 stars · 468 forks

AI VISIBILITY SCORE
22 /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
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 databricks-academy/large-language-models, 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 opening to emphasize official course and Databricks focus

    Why:

    CURRENT
    ## Large Language Models

This repo contains the notebooks and slides for the Large Language Models: Application through Production course on edX & Databricks Academy.
    COPY-PASTE FIX
    ## Official Databricks Academy Course: Large Language Models (LLMs) Application through Production

This repository provides the official notebooks and slides for the **Large Language Models: Application through Production** course, offered on edX and Databricks Academy. It's designed for data scientists and ML engineers seeking hands-on, Databricks-specific examples to build and deploy LLM applications.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    large-language-models, llm, databricks, machine-learning, mlops, education, course-materials, notebooks, generative-ai
  • mediumhomepage#3
    Add a homepage URL

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://www.databricks.com/academy/courses/large-language-models-application-through-production

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-academy/large-language-models
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. LangChain · recommended 1×
  3. openai/openai-cookbook · recommended 1×
  4. DeepLearning.AI Courses · recommended 1×
  5. Kaggle · recommended 1×
  • CATEGORY QUERY
    Where can I find practical notebooks to learn about large language model applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Examples (huggingface/transformers)
    2. LangChain
    3. OpenAI Cookbook (openai/openai-cookbook)
    4. DeepLearning.AI Courses
    5. Kaggle
    6. Google Cloud Vertex AI Workbench
    7. Awesome-LLM-Apps (Mooler0410/Awesome-LLM-Apps)

    AI recommended 7 alternatives but never named databricks-academy/large-language-models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking hands-on examples and best practices for deploying large language models into production.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Optimum
    3. Hugging Face Inference Endpoints
    4. MLflow
    5. Kubernetes
    6. KServe
    7. Seldon Core
    8. NVIDIA Triton Inference Server
    9. AWS SageMaker
    10. Google Cloud Vertex AI
    11. Microsoft Azure Machine Learning

    AI recommended 11 alternatives but never named databricks-academy/large-language-models. 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 databricks-academy/large-language-models?
    pass
    AI did not name databricks-academy/large-language-models — 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 databricks-academy/large-language-models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named databricks-academy/large-language-models 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-academy/large-language-models solve, and who is the primary audience?
    pass
    AI did not name databricks-academy/large-language-models — 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?

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