RRepoGEO

REPOGEO REPORT · LITE

varungodbole/prompt-tuning-playbook

Default branch main · commit 2a929523 · scanned 6/11/2026, 9:32:36 AM

GitHub: 900 stars · 38 forks

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 varungodbole/prompt-tuning-playbook, 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
    Add a clear positioning statement to the README's introduction

    Why:

    CURRENT
    The current README starts with "# LLM Prompt Tuning Playbook" and then lists authors and a table of contents, followed by "Who is this document for?".
    COPY-PASTE FIX
    Add a sentence immediately after the title/authors, such as: "This is a comprehensive guide and practical playbook for crafting effective prompts for post-trained Large Language Models, distinct from LLM libraries or frameworks."
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, prompt-engineering, prompt-tuning, large-language-models, ai-best-practices, guide, playbook
  • mediumreadme#3
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, for example: "This project is licensed under the terms specified in the `LICENSE` file. Please refer to that file for full details."

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 varungodbole/prompt-tuning-playbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Pinecone · recommended 2×
  4. Weaviate · recommended 2×
  5. Hugging Face Transformers · recommended 2×
  • CATEGORY QUERY
    How can I improve the quality and relevance of responses from large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Guidance
    4. Pinecone
    5. Weaviate
    6. Chroma
    7. OpenAI API
    8. Hugging Face Transformers
    9. PEFT
    10. bitsandbytes
    11. Google Cloud Vertex AI
    12. Arize AI
    13. WhyLabs

    AI recommended 13 alternatives but never named varungodbole/prompt-tuning-playbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices and guidelines for crafting effective prompts for LLMs?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Fine-tuning API
    2. Hugging Face Transformers
    3. LangChain
    4. LlamaIndex
    5. Microsoft Guidance
    6. Pinecone
    7. Weaviate

    AI recommended 7 alternatives but never named varungodbole/prompt-tuning-playbook. 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 varungodbole/prompt-tuning-playbook?
    pass
    AI named varungodbole/prompt-tuning-playbook explicitly

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

  • If a team adopts varungodbole/prompt-tuning-playbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named varungodbole/prompt-tuning-playbook 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 varungodbole/prompt-tuning-playbook solve, and who is the primary audience?
    pass
    AI did not name varungodbole/prompt-tuning-playbook — 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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varungodbole/prompt-tuning-playbook — 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