RRepoGEO

REPOGEO REPORT · LITE

Farzad-R/LLM-Zero-to-Hundred

Default branch master · commit 2bfd3a1d · scanned 5/29/2026, 5:47:55 PM

GitHub: 555 stars · 225 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 Farzad-R/LLM-Zero-to-Hundred, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the root of the repository to clearly state the terms of use.
  • mediumreadme#2
    Refine the README's opening sentence to emphasize its 'learning roadmap' nature

    Why:

    CURRENT
    This repository showcases various applications of LLM chatbots and provides comprehensive insights into established methodologies for training and fine-tuning Language Models.
    COPY-PASTE FIX
    This repository serves as a structured, progressive learning roadmap for Large Language Models (LLMs), guiding users from foundational concepts to advanced applications through various chatbot projects (RAG, LLM agents, etc.) and well-known techniques for training and fine-tuning LLMs.

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 Farzad-R/LLM-Zero-to-Hundred
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. huggingface/transformers · recommended 2×
  3. run-llama/llama_index · recommended 2×
  4. OpenAI Cookbook · recommended 1×
  5. DeepLearning.AI · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive examples and tutorials for building LLM-powered applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. OpenAI Cookbook
    3. Hugging Face Transformers (huggingface/transformers)
    4. DeepLearning.AI
    5. LlamaIndex (run-llama/llama_index)
    6. Weights & Biases (wandb/wandb)

    AI recommended 6 alternatives but never named Farzad-R/LLM-Zero-to-Hundred. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical guides and code examples for implementing RAG or fine-tuning large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers & Datasets Libraries
    2. Transformers (huggingface/transformers)
    3. Datasets (huggingface/datasets)
    4. PEFT (huggingface/peft)
    5. Hugging Face Course
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)
    8. OpenAI Cookbook (openai/openai-cookbook)
    9. PyTorch Lightning (Lightning-AI/lightning)
    10. Keras (keras-team/keras)
    11. TensorFlow Hub
    12. DeepLearning.AI Courses

    AI recommended 12 alternatives but never named Farzad-R/LLM-Zero-to-Hundred. 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 Farzad-R/LLM-Zero-to-Hundred?
    pass
    AI named Farzad-R/LLM-Zero-to-Hundred explicitly

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

  • If a team adopts Farzad-R/LLM-Zero-to-Hundred in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Farzad-R/LLM-Zero-to-Hundred 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 Farzad-R/LLM-Zero-to-Hundred solve, and who is the primary audience?
    pass
    AI did not name Farzad-R/LLM-Zero-to-Hundred — 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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Farzad-R/LLM-Zero-to-Hundred — 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