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microsoft/generative-ai-for-beginners

21 Lessons, Get Started Building with Generative AI

7.8 Moderate pattern signal View on GitHub

Maintenance and Governance Review

This report summarizes scanner evidence for microsoft/generative-ai-for-beginners, a Jupyter Notebook project with 121,122 GitHub stars. SynthScan examined 537,011 lines of code across 2470 source files, recording 1135 pattern matches distributed across 14 syntactic categories. The legacy adjusted pattern signal is 7.8 (Moderate pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

7.8
Adjusted Signal
7.8
Raw Pattern Score
100%
Time Factor
2026-10-08
Last Push
121.1K
Stars
Jupyter Notebook
Language
537.0K
Lines of Code
2.5K
Files
1.1K
Pattern Hits
2026-10-12
Scan Date
0.23
HC Hit Rate

Risk Review · v3

Use each vector to guide manual verification of the underlying findings.

Cognitive Ownership Risk 11.0/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 436 finding(s).

Hallucinated Dependency & Supply Chain Risk 0.2/100 · LOW

Are imports declared and package/API names verified? 19 finding(s).

Agent Over-Engineering Ratio 24.0/100 · LOW

Are wrappers, guards, and repeated structures necessary? 708 finding(s).

PR Review Velocity Impact 35.7/100 · MEDIUM

Which changes need focused human review? 1135 finding(s).

What These Metrics Mean

Adjusted Signal
Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score
Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor
A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits
Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate
High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files
Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 573MEDIUM 369LOW 193

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 1135 distinct pattern matches across 14 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Cross-File Repetition558 hits · 2790 pts
SeverityFileLineSnippetContext
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HIGHtranslations/fr/05-advanced-prompts/README.md0<h1>books api</h1> <p>a prototype api for retrieving books.</p>STRING
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498 more matches not shown…
Modern AI Meta-Vocabulary365 hits · 1091 pts
SeverityFileLineSnippetContext
MEDIUM10-building-low-code-ai-applications/README.md75[Microsoft Copilot Studio](https://learn.microsoft.com/microsoft-copilot-studio/fundamentals-what-is-copilot-studio?WT.mCODE
MEDIUM20-mistral/README.md33### RAG Example using Mistral Large 2COMMENT
MEDIUM02-exploring-and-comparing-different-llms/README.md163### Retrieval Augmented Generation (RAG)COMMENT
MEDIUM15-rag-and-vector-databases/README.md1# Retrieval Augmented Generation (RAG) and Vector DatabasesCOMMENT
MEDIUM15-rag-and-vector-databases/README.md41## Retrieval Augmented Generation (RAG)COMMENT
MEDIUM15-rag-and-vector-databases/README.md237## Use Cases for using RAG (Retrieval Augmented Generation) and vector databasesCOMMENT
MEDIUMtranslations/sl/20-mistral/README.md33### Primer RAG z uporabo Mistral Large 2COMMENT
MEDIUM…sl/02-exploring-and-comparing-different-llms/README.md164### Retrieval Augmented Generation (RAG)COMMENT
MEDIUMtranslations/sl/15-rag-and-vector-databases/README.md1# Generiranje z nadgradnjo pridobivanja (RAG) in vektorske baze podatkovCOMMENT
MEDIUMtranslations/sl/15-rag-and-vector-databases/README.md41## Generiranje z nadgradnjo pridobivanja (RAG)COMMENT
MEDIUMtranslations/sl/15-rag-and-vector-databases/README.md45### Kako delujejo RAG (Generiranje z nadgradnjo pridobivanja)COMMENT
MEDIUMtranslations/sl/15-rag-and-vector-databases/README.md69### Zakaj uporabljati RAG?COMMENT
MEDIUMtranslations/sl/15-rag-and-vector-databases/README.md237## Primeri uporabe RAG (Generiranje z nadgradnjo pridobivanja) in vektorskih baz podatkovCOMMENT
MEDIUMtranslations/sk/20-mistral/README.md33### Príklad RAG s použitím Mistral Large 2COMMENT
MEDIUM…sk/02-exploring-and-comparing-different-llms/README.md164### Retrieval Augmented Generation (RAG)COMMENT
MEDIUMtranslations/sk/15-rag-and-vector-databases/README.md1# Retrieval Augmented Generation (RAG) a vektorové databázyCOMMENT
MEDIUMtranslations/sk/15-rag-and-vector-databases/README.md41## Retrieval Augmented Generation (RAG)COMMENT
MEDIUMtranslations/sk/15-rag-and-vector-databases/README.md45### Ako fungujú RAG (Retrieval Augmented Generation)COMMENT
MEDIUMtranslations/sk/15-rag-and-vector-databases/README.md69### Prečo používať RAG?COMMENT
MEDIUMtranslations/sk/15-rag-and-vector-databases/README.md237## Príklady využitia RAG (Retrieval Augmented Generation) a vektorových databázCOMMENT
MEDIUMtranslations/ur/20-mistral/README.md33### میسٹرال لارجر 2 کا استعمال کرتے ہوئے RAG کی مثالCOMMENT
MEDIUM…ur/02-exploring-and-comparing-different-llms/README.md164### ریٹریول آگمینٹڈ جنریشن (RAG)COMMENT
MEDIUMtranslations/ur/15-rag-and-vector-databases/README.md1# بازیافت سے تقویت یافتہ تخلیق (RAG) اور ویکٹر ڈیٹا بیسزCOMMENT
MEDIUMtranslations/ur/15-rag-and-vector-databases/README.md41## بازیافت سے تقویت یافتہ تخلیق (RAG)COMMENT
MEDIUMtranslations/ur/15-rag-and-vector-databases/README.md237## RAG (بازیافت سے تقویت یافتہ تخلیق) اور ویکٹر ڈیٹا بیسز کے استعمال کے معاملاتCOMMENT
MEDIUMtranslations/sw/20-mistral/README.md33### Mfano wa RAG ukitumia Mistral Large 2COMMENT
MEDIUM…sw/02-exploring-and-comparing-different-llms/README.md164### Uzalishaji Ulioboreshwa Kwa Marejeleo (RAG)COMMENT
MEDIUMtranslations/sw/15-rag-and-vector-databases/README.md1# Uundaji Ulioboreshwa kwa Kupata Taarifa (RAG) na Hifadhidata za VectorCOMMENT
MEDIUMtranslations/sw/15-rag-and-vector-databases/README.md41## Uundaji Ulioboreshwa kwa Kupata Taarifa (RAG)COMMENT
MEDIUMtranslations/sw/15-rag-and-vector-databases/README.md45### Jinsi RAG (Uundaji Ulioboreshwa kwa Kupata Taarifa) inavyofanya kaziCOMMENT
MEDIUMtranslations/sw/15-rag-and-vector-databases/README.md69### Kwa nini utumie RAG?COMMENT
MEDIUMtranslations/sw/15-rag-and-vector-databases/README.md237## Matumizi ya RAG (Uundaji Ulioboreshwa kwa Kupata Taarifa) na hifadhidata za vectorCOMMENT
MEDIUMtranslations/pl/20-mistral/README.md33### Przykład RAG z użyciem Mistral Large 2COMMENT
MEDIUM…pl/02-exploring-and-comparing-different-llms/README.md164### Retrieval Augmented Generation (RAG)COMMENT
MEDIUMtranslations/pl/15-rag-and-vector-databases/README.md1# Generowanie Wzbogacone o Wyszukiwanie (RAG) i Bazy Danych WektorowychCOMMENT
MEDIUMtranslations/pl/15-rag-and-vector-databases/README.md41## Generowanie Wzbogacone o Wyszukiwanie (RAG)COMMENT
MEDIUMtranslations/pl/15-rag-and-vector-databases/README.md45### Jak działają RAG (Generowanie Wzbogacone o Wyszukiwanie)COMMENT
MEDIUMtranslations/pl/15-rag-and-vector-databases/README.md69### Dlaczego warto używać RAG?COMMENT
MEDIUMtranslations/pl/15-rag-and-vector-databases/README.md237## Przypadki użycia RAG (Generowanie Wzbogacone o Wyszukiwanie) i baz danych wektorowychCOMMENT
MEDIUMtranslations/vi/20-mistral/README.md33### Ví dụ RAG sử dụng Mistral Large 2COMMENT
MEDIUM…vi/02-exploring-and-comparing-different-llms/README.md164### Tạo sinh tăng cường truy xuất (RAG)COMMENT
MEDIUMtranslations/vi/15-rag-and-vector-databases/README.md1# Tạo sinh tăng cường truy xuất (RAG) và cơ sở dữ liệu vectorCOMMENT
MEDIUMtranslations/vi/15-rag-and-vector-databases/README.md41## Tạo sinh tăng cường truy xuất (RAG)COMMENT
MEDIUMtranslations/vi/15-rag-and-vector-databases/README.md45### Cách RAG (Tạo sinh tăng cường truy xuất) hoạt độngCOMMENT
MEDIUMtranslations/vi/15-rag-and-vector-databases/README.md69### Tại sao bạn nên dùng RAG?COMMENT
MEDIUMtranslations/vi/15-rag-and-vector-databases/README.md237## Các trường hợp sử dụng RAG (Tạo sinh tăng cường truy xuất) và cơ sở dữ liệu vectorCOMMENT
MEDIUMtranslations/sv/20-mistral/README.md33### RAG-exempel med Mistral Large 2COMMENT
MEDIUM…sv/02-exploring-and-comparing-different-llms/README.md164### Retrieval Augmented Generation (RAG)COMMENT
MEDIUMtranslations/sv/15-rag-and-vector-databases/README.md1# Retrieval Augmented Generation (RAG) och vektordatabaserCOMMENT
MEDIUMtranslations/sv/15-rag-and-vector-databases/README.md41## Retrieval Augmented Generation (RAG)COMMENT
MEDIUMtranslations/sv/15-rag-and-vector-databases/README.md45### Hur RAG (Retrieval Augmented Generation) fungerarCOMMENT
MEDIUMtranslations/sv/15-rag-and-vector-databases/README.md69### Varför skulle man använda RAG?COMMENT
MEDIUMtranslations/sv/15-rag-and-vector-databases/README.md237## Användningsområden för att använda RAG (Retrieval Augmented Generation) och vektordatabaserCOMMENT
MEDIUMtranslations/he/20-mistral/README.md33### דוגמה ל-RAG באמצעות Mistral Large 2COMMENT
MEDIUM…he/02-exploring-and-comparing-different-llms/README.md164### Retrieval Augmented Generation (RAG)COMMENT
MEDIUMtranslations/he/15-rag-and-vector-databases/README.md1# יצירה מוגברת על ידי שליפה (RAG) ומסדי נתונים וקטורייםCOMMENT
MEDIUMtranslations/he/15-rag-and-vector-databases/README.md41## יצירה מוגברת על ידי שליפה (RAG)COMMENT
MEDIUMtranslations/he/15-rag-and-vector-databases/README.md237## מקרים לשימוש ב-RAG ומסדי נתונים וקטורייםCOMMENT
MEDIUMtranslations/ms/20-mistral/README.md33### Contoh RAG menggunakan Mistral Large 2COMMENT
MEDIUM…ms/02-exploring-and-comparing-different-llms/README.md164### Retrieval Augmented Generation (RAG)COMMENT
305 more matches not shown…
Excessive Try-Catch Wrapping115 hits · 115 pts
SeverityFileLineSnippetContext
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Docstring Block Structure11 hits · 55 pts
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Hyper-Verbose Identifiers28 hits · 38 pts
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Structural Annotation Overuse21 hits · 29 pts
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Synthetic Comment Markers4 hits · 20 pts
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Unused Imports18 hits · 18 pts
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Self-Referential Comments4 hits · 10 pts
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MEDIUM…ing-search-applications/scripts/transcript_download.py135# Create a request object with the playlist ID and the max resultsCOMMENT
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Modern Structural Boilerplate7 hits · 7 pts
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Redundant / Tautological Comments1 hit · 2 pts
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LOW…ing-search-applications/scripts/transcript_download.py146 # Iterate over the items in the response and append the video IDs to the listCOMMENT
Fake / Example Data1 hit · 2 pts
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Deep Nesting1 hit · 1 pts
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Over-Commented Block1 hit · 1 pts
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