REPOGEO REPORT · LITE

jackaduma/awesome_LLMs_interview_notes

Default branch main · commit af09cf27 · scanned 5/21/2026, 12:38:27 AM

GitHub: 1,315 stars · 330 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

22 /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

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 jackaduma/awesome_LLMs_interview_notes, 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

highreadme#1

Reposition the README H1 to specify category

Why:

CURRENT

# awesome_LLMs_interview_notes
LLMs interview notes and answers

COPY-PASTE FIX

# awesome_LLMs_interview_notes
Comprehensive interview notes and answers for Large Language Model (LLM) algorithm engineer roles.

mediumabout#2
Add a homepage URL to the About section
Why:
COPY-PASTE FIX
```
https://your-related-blog-or-resource.com
```

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 jackaduma/awesome_LLMs_interview_notes

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Designing Machine Learning Systems

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Designing Machine Learning Systems · recommended 1×
Dive into Deep Learning · recommended 1×
Hugging Face Transformers Library · recommended 1×
`transformers` library · recommended 1×
`tokenizers` · recommended 1×

CATEGORY QUERY
Where can I find comprehensive interview preparation materials for large language model roles?
you: not recommended
AI recommended (in order):
1. Designing Machine Learning Systems
2. Dive into Deep Learning
3. Hugging Face Transformers Library
4. `transformers` library
5. `tokenizers`
6. `datasets` library
7. DeepLearning.AI's 'Generative AI with Large Language Models' Specialization
8. The Illustrated Transformer
9. Papers With Code
10. Google's 'Machine Learning Crash Course'
AI recommended 10 alternatives but never named jackaduma/awesome_LLMs_interview_notes. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the essential topics and common questions for an LLM algorithm engineer interview?
you: not recommended
AI recommended (in order):
1. GPT
2. BERT
3. T5
4. LLaMA
5. Mistral
6. Word2Vec
7. GloVe
8. AdamW
9. LoRA
10. QLoRA
11. PEFT (huggingface/peft)
12. Hugging Face Transformers (huggingface/transformers)
13. vLLM (vllm-project/vllm)
14. TGI (huggingface/text-generation-inference)
15. Triton Inference Server (NVIDIA/triton-inference-server)
16. PyTorch (pytorch/pytorch)
17. TensorFlow (tensorflow/tensorflow)
AI recommended 17 alternatives but never named jackaduma/awesome_LLMs_interview_notes. 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 jackaduma/awesome_LLMs_interview_notes?
pass
AI did not name jackaduma/awesome_LLMs_interview_notes — 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 jackaduma/awesome_LLMs_interview_notes in production, what risks or prerequisites should they evaluate first?
pass
AI named jackaduma/awesome_LLMs_interview_notes 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 jackaduma/awesome_LLMs_interview_notes solve, and who is the primary audience?
pass
AI did not name jackaduma/awesome_LLMs_interview_notes — 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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MARKDOWN (README)

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jackaduma/awesome_LLMs_interview_notes — 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