RRepoGEO

REPOGEO REPORT · LITE

build-with-groq/g1

Default branch main · commit 854e6944 · scanned 6/24/2026, 4:18:09 PM

GitHub: 4,179 stars · 360 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 build-with-groq/g1, 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 the README's opening paragraph to explicitly state the project's type and what it is not

    Why:

    CURRENT
    This is an early prototype of using prompting strategies to improve the LLM's reasoning capabilities through o1-like reasoning chains. This allows the LLM to "think" and solve logical problems that usually otherwise stump leading models. Unlike o1, all the reasoning tokens are shown, and the app uses an open source model.
    COPY-PASTE FIX
    This repository, `g1`, is an experimental prototype focused on advanced prompting strategies to improve LLM reasoning capabilities through o1-like reasoning chains. It demonstrates how open-source models like Llama-3.1 70b on Groq can "think" and solve complex logical problems, and is *not* a client library for the Groq API or a general chat UI framework.
  • hightopics#2
    Add relevant topics to accurately categorize the repository

    Why:

    CURRENT
    managed-by-terraform
    COPY-PASTE FIX
    llm-reasoning, prompt-engineering, chain-of-thought, groq-api, llama-3-1, ai-experiment, open-source-llm, llm-visualization
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/build-with-groq/g1

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 build-with-groq/g1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ReAct
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ReAct · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. LangSmith · recommended 1×
  4. run-llama/llama_index · recommended 1×
  5. gradio-app/gradio · recommended 1×
  • CATEGORY QUERY
    How to improve large language model logical reasoning using prompting techniques?
    you: not recommended
    AI recommended (in order):
    1. ReAct

    AI recommended 1 alternative but never named build-with-groq/g1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for visualizing LLM chain of thought and problem-solving steps with open models?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LangSmith
    3. LlamaIndex (run-llama/llama_index)
    4. Gradio (gradio-app/gradio)
    5. Streamlit (streamlit/streamlit)
    6. Weights & Biases Prompts (wandb/wandb)
    7. TensorBoard (tensorflow/tensorboard)
    8. Graphviz (ellson/graphviz)
    9. Mermaid (mermaid-js/mermaid)

    AI recommended 9 alternatives but never named build-with-groq/g1. 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 build-with-groq/g1?
    pass
    AI did not name build-with-groq/g1 — 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 build-with-groq/g1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named build-with-groq/g1 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 build-with-groq/g1 solve, and who is the primary audience?
    pass
    AI named build-with-groq/g1 explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite