RRepoGEO

REPOGEO REPORT · LITE

jamwithai/production-agentic-rag-course

Default branch main · commit 424a0eb9 · scanned 6/20/2026, 3:47:27 AM

GitHub: 6,933 stars · 1,554 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
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 jamwithai/production-agentic-rag-course, 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
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    A hands-on course to build production-grade agentic RAG systems, focusing on industry best practices from keyword search to hybrid retrieval.
  • mediumreadme#2
    Rephrase README's initial heading to emphasize 'course'

    Why:

    CURRENT
    # The Mother of AI Project
## Phase 1 RAG Systems: arXiv Paper Curator
    COPY-PASTE FIX
    # Production Agentic RAG Course: The arXiv Paper Curator Project
## A Learner-Focused Journey into Building Production RAG Systems

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 jamwithai/production-agentic-rag-course
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. langchain-ai/langchain · recommended 2×
  4. huggingface/transformers · recommended 2×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How can I learn to build robust retrieval-augmented generation systems for production?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. Keras
    4. Pinecone
    5. Weaviate (weaviate/weaviate)
    6. Qdrant (qdrant/qdrant)
    7. Chroma (chroma-core/chroma)
    8. LlamaIndex (run-llama/llama_index)
    9. LangChain (langchain-ai/langchain)
    10. Hugging Face Transformers (huggingface/transformers)
    11. Hugging Face Datasets (huggingface/datasets)
    12. Hugging Face Hub
    13. Docker
    14. Kubernetes (kubernetes/kubernetes)
    15. MLflow (mlflow/mlflow)
    16. AWS SageMaker
    17. Google Cloud Vertex AI
    18. Azure Machine Learning

    AI recommended 18 alternatives but never named jamwithai/production-agentic-rag-course. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for implementing hybrid search in RAG applications?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Embeddings
    2. Cohere Embeddings
    3. Sentence-BERT
    4. Elasticsearch
    5. Pinecone
    6. Weaviate
    7. OpenSearch
    8. Faiss (facebookresearch/faiss)
    9. Apache Lucene
    10. Apache Solr
    11. Google T5
    12. Microsoft DeBERTa
    13. Cohere Rerank
    14. Hugging Face Transformers library (huggingface/transformers)
    15. Neo4j
    16. SQL Database
    17. Milvus
    18. Zilliz Cloud
    19. GPT-3.5
    20. Llama 2
    21. WordNet
    22. Thesaurus
    23. ANCE
    24. ColBERT
    25. LlamaIndex (run-llama/llama_index)
    26. LangChain (langchain-ai/langchain)

    AI recommended 26 alternatives but never named jamwithai/production-agentic-rag-course. 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 jamwithai/production-agentic-rag-course?
    pass
    AI did not name jamwithai/production-agentic-rag-course — 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 jamwithai/production-agentic-rag-course in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jamwithai/production-agentic-rag-course 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 jamwithai/production-agentic-rag-course solve, and who is the primary audience?
    pass
    AI did not name jamwithai/production-agentic-rag-course — 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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jamwithai/production-agentic-rag-course — 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