RRepoGEO

REPOGEO REPORT · LITE

JindongGu/Awesome-Prompting-on-Vision-Language-Model

Default branch main · commit 0c381439 · scanned 6/9/2026, 8:52:45 AM

GitHub: 510 stars · 38 forks

AI VISIBILITY SCORE
15 /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
0 / 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 JindongGu/Awesome-Prompting-on-Vision-Language-Model, 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 clarify it's a survey/awesome list of papers

    Why:

    CURRENT
    This repo lists relevant papers summarized in our survey: A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models.
    COPY-PASTE FIX
    This repository is a curated, comprehensive survey of cutting-edge research papers on prompt engineering for Vision-Language Models (VLMs). It accompanies our survey paper: 'A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models'.
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory, e.g., with the MIT License text.
  • mediumtopics#3
    Expand repository topics for better categorization as a survey/list

    Why:

    CURRENT
    foundation-models, prompt-engineering, vision-and-language
    COPY-PASTE FIX
    foundation-models, prompt-engineering, vision-and-language, awesome-list, survey-papers, research-papers, vlm-prompting

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 JindongGu/Awesome-Prompting-on-Vision-Language-Model
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI's GPT-4V
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI's GPT-4V · recommended 1×
  2. Google's Gemini · recommended 1×
  3. Anthropic's Claude 3 · recommended 1×
  4. LLaVA · recommended 1×
  • CATEGORY QUERY
    How can I improve performance of vision-language models using prompt engineering techniques?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's GPT-4V
    2. Google's Gemini
    3. Anthropic's Claude 3
    4. LLaVA

    AI recommended 4 alternatives but never named JindongGu/Awesome-Prompting-on-Vision-Language-Model. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a survey of prompt engineering methods for various vision-language tasks?
    you: not recommended
    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 JindongGu/Awesome-Prompting-on-Vision-Language-Model?
    pass
    AI did not name JindongGu/Awesome-Prompting-on-Vision-Language-Model — 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 JindongGu/Awesome-Prompting-on-Vision-Language-Model in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name JindongGu/Awesome-Prompting-on-Vision-Language-Model — 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?

  • In one sentence, what problem does the repo JindongGu/Awesome-Prompting-on-Vision-Language-Model solve, and who is the primary audience?
    pass
    AI did not name JindongGu/Awesome-Prompting-on-Vision-Language-Model — 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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JindongGu/Awesome-Prompting-on-Vision-Language-Model — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
JindongGu/Awesome-Prompting-on-Vision-Language-Model — RepoGEO report