RRepoGEO

REPOGEO REPORT · LITE

ombharatiya/ai-system-design-guide

Default branch main · commit df612278 · scanned 7/1/2026, 4:43:03 PM

GitHub: 1,956 stars · 398 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
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 ombharatiya/ai-system-design-guide, 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
    Add a clear introductory sentence to the README distinguishing the guide from tools

    Why:

    COPY-PASTE FIX
    Add this as the very first line of the README, after the title/subtitle:

This repository serves as a comprehensive, continuously updated *textual guide* and *interview preparation resource* for AI engineers, distinct from MLOps tools or executable frameworks.
  • mediumtopics#2
    Add topics that explicitly describe the repo's format and purpose

    Why:

    CURRENT
    agentic-ai, agentic-workflow, ai, ai-jobs, artificial-intelligence, aws, azure, claude, evals, forward-deployed-engineer, gemini, gen-ai, interview, interview-questions, llm, machine-learning, natural-language-processing, open-ai, rag, system-design-interview
    COPY-PASTE FIX
    agentic-ai, agentic-workflow, ai, ai-jobs, artificial-intelligence, aws, azure, claude, evals, forward-deployed-engineer, gemini, gen-ai, interview, interview-questions, llm, machine-learning, natural-language-processing, open-ai, rag, system-design-interview, ai-system-design-guide, ai-interview-prep, ai-engineering-handbook, production-ai-reference
  • mediumcomparison#3
    Add a 'How is this different?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README:

## How is this guide different from MLOps tools or general system design books?

This guide is a comprehensive *textual reference* for the *conceptual design* and *interview preparation* of AI systems. Unlike MLOps platforms (e.g., MLflow, DVC, Weights & Biases), it does not provide executable code, tracking, or deployment infrastructure. Unlike general system design books, it focuses specifically on the unique challenges and patterns of *AI-driven systems*, including LLMs, RAG, and agentic architectures.

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 ombharatiya/ai-system-design-guide
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
iterative/dvc
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. iterative/dvc · recommended 1×
  2. mlflow/mlflow · recommended 1×
  3. pachyderm/pachyderm · recommended 1×
  4. wandb/wandb · recommended 1×
  5. comet-ml/comet-python-sdk · recommended 1×
  • CATEGORY QUERY
    What are best practices for designing robust AI systems for production deployment and evaluation?
    you: not recommended
    AI recommended (in order):
    1. DVC (iterative/dvc)
    2. MLflow (mlflow/mlflow)
    3. Pachyderm (pachyderm/pachyderm)
    4. Weights & Biases (wandb/wandb)
    5. Comet ML (comet-ml/comet-python-sdk)
    6. Kubeflow Metadata (kubeflow/kubeflow)
    7. SageMaker Model Registry
    8. Kubeflow Pipelines (kubeflow/pipelines)
    9. Apache Airflow (apache/airflow)
    10. GitHub Actions
    11. GitLab CI/CD
    12. Azure DevOps
    13. Google Cloud Build
    14. Prometheus (prometheus/prometheus)
    15. Grafana (grafana/grafana)
    16. Datadog
    17. New Relic
    18. Fiddler AI
    19. Arize AI
    20. SHAP (shap/shap)
    21. LIME (marcotcr/lime)
    22. InterpretML (interpretml/interpretml)
    23. What-If Tool (tensorflow/tensorboard)
    24. Adversarial Robustness Toolbox (Trusted-AI/adversarial-robustness-toolbox)
    25. CleverHans (cleverhans-lab/cleverhans)

    AI recommended 25 alternatives but never named ombharatiya/ai-system-design-guide. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to prepare for an AI system design interview focused on large language models and RAG?
    you: not recommended
    AI recommended (in order):
    1. Grokking the System Design Interview
    2. Designing Data-Intensive Applications
    3. System Design Interview - An insider's guide
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. GPT-3/4
    6. Llama 2 (meta-llama/llama-models)
    7. Mixtral (mistralai/mistral-src)
    8. PaLM 2/Gemini
    9. OpenAI API
    10. LangChain (langchain-ai/langchain)
    11. LlamaIndex (run-llama/llama_index)
    12. Pinecone
    13. Weaviate (weaviate/weaviate)
    14. Milvus (milvus-io/milvus)
    15. Chroma (chroma-core/chroma)
    16. OpenAI Embeddings
    17. Sentence-BERT (UKPLab/sentence-transformers)
    18. Cohere Embeddings

    AI recommended 18 alternatives but never named ombharatiya/ai-system-design-guide. 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 ombharatiya/ai-system-design-guide?
    pass
    AI did not name ombharatiya/ai-system-design-guide — 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 ombharatiya/ai-system-design-guide in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ombharatiya/ai-system-design-guide 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 ombharatiya/ai-system-design-guide solve, and who is the primary audience?
    pass
    AI did not name ombharatiya/ai-system-design-guide — 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

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ombharatiya/ai-system-design-guide — 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