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REPOGEO REPORT · LITE

curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain

Default branch master · commit 2823fd0f · scanned 5/23/2026, 3:27:53 PM

GitHub: 1,242 stars · 376 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)

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

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain, 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 to clearly state it's a tutorial/project collection

    Why:

    CURRENT
    # Get SH*T Done with Prompt Engineering and LangChain

Build real-world AI apps with ChatGPT/GPT-4 and LangChain in Python
    COPY-PASTE FIX
    # Get SH*T Done with Prompt Engineering and LangChain: Practical Tutorials & Real-World Projects

This repository offers hands-on tutorials and complete project examples for building AI applications with ChatGPT/GPT-4 and LangChain in Python. Learn to leverage prompt engineering and Large Language Models (LLMs) like Llama 2 to work with your custom data effectively.
  • mediumreadme#2
    Add a dedicated 'What is this repository?' section to the README

    Why:

    COPY-PASTE FIX
    ## What is this repository?
This repository is a comprehensive collection of Jupyter notebooks, practical tutorials, and complete project examples designed to teach you how to build real-world AI applications. You'll learn prompt engineering techniques and how to use frameworks like LangChain with Large Language Models (LLMs) such as ChatGPT, GPT-4, and Llama 2, especially for working with custom datasets.
  • mediumtopics#3
    Refine topics to explicitly include 'tutorials' and 'projects'

    Why:

    CURRENT
    artificial-intelligence, chatgpt, deep-learning, gpt-4, gpt4, langchain, language-models, large-language-models, llama2, openai, prompt-engineering, python
    COPY-PASTE FIX
    artificial-intelligence, chatgpt, deep-learning, gpt-4, gpt4, langchain, language-models, large-language-models, llama2, openai, prompt-engineering, python, tutorials, ai-projects, machine-learning-tutorials

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 curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. Hugging Face · recommended 1×
  4. Transformers · recommended 1×
  5. Datasets · recommended 1×
  • CATEGORY QUERY
    How to build AI applications that query my own specific datasets effectively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face
    4. Transformers
    5. Datasets
    6. OpenAI API
    7. Embeddings API
    8. Chat Completions API
    9. Pinecone
    10. Weaviate
    11. ChromaDB
    12. Weights & Biases
    13. MLflow

    AI recommended 13 alternatives but never named curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical tutorials for prompt engineering to develop real-world AI solutions.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Cookbook
    2. DeepLearning.AI
    3. LangChain (langchain-ai/langchain)
    4. Anthropic
    5. Google Cloud Skills Boost
    6. Hugging Face Transformers Library (huggingface/transformers)

    AI recommended 6 alternatives but never named curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain?
    pass
    AI did not name curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain — 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 curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain in production, what risks or prerequisites should they evaluate first?
    pass
    AI named curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain 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 curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain solve, and who is the primary audience?
    pass
    AI did not name curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain — 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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curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain — 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