RRepoGEO

REPOGEO REPORT · LITE

hamzafarooq/building-llm-applications-from-scratch

Default branch main · commit fbb74e61 · scanned 5/28/2026, 12:42:33 AM

GitHub: 2,386 stars · 653 forks

AI VISIBILITY SCORE
17 /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
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 hamzafarooq/building-llm-applications-from-scratch, 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
  • highabout#1
    Update the repository description to clarify its nature as a course

    Why:

    CURRENT
    Code and Slides
    COPY-PASTE FIX
    An open-sourced course with code and slides for building Large Language Model (LLM) applications from scratch, covering Transformer Architecture, RAG, and open-source LLM deployment.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).

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 hamzafarooq/building-llm-applications-from-scratch
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. Hugging Face Transformers · recommended 1×
  4. Faiss · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to build custom LLM applications from scratch, understanding core architecture and retrieval systems?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. Faiss
    5. Pinecone
    6. Weaviate
    7. Qdrant
    8. PyTorch
    9. TensorFlow

    AI recommended 9 alternatives but never named hamzafarooq/building-llm-applications-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking resources to learn Retrieval-Augmented Generation and deploy open-source large language models.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers Library
    4. Ollama
    5. Instructor Embeddings
    6. vLLM
    7. Triton Inference Server

    AI recommended 7 alternatives but never named hamzafarooq/building-llm-applications-from-scratch. 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 hamzafarooq/building-llm-applications-from-scratch?
    pass
    AI did not name hamzafarooq/building-llm-applications-from-scratch — 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 hamzafarooq/building-llm-applications-from-scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hamzafarooq/building-llm-applications-from-scratch 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 hamzafarooq/building-llm-applications-from-scratch solve, and who is the primary audience?
    pass
    AI did not name hamzafarooq/building-llm-applications-from-scratch — 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

Drop this badge into the README of hamzafarooq/building-llm-applications-from-scratch. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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hamzafarooq/building-llm-applications-from-scratch — 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