# What Programming Languages Are Used in the AI Engineering from Scratch Repository? > Explore the AI Engineering from Scratch repository's core programming languages: Python, TypeScript, Rust, and Julia. Discover how a stdlib-first approach ensures clarity and limits dependencies. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: getting-started - Published: 2026-08-26 --- **The AI Engineering from Scratch repository uses Python, TypeScript, Rust, and Julia as its four core programming languages, following a strict "stdlib-first" policy defined in [`AGENTS.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENTS.md) that limits dependencies and enforces pedagogical clarity.** The [rohitg00/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) curriculum teaches AI engineering concepts from first principles across multiple implementation phases. While the educational content remains deliberately language-agnostic, the codebase adheres to a strict standard library-first approach that restricts each lesson to one of four carefully selected languages, ensuring learners understand foundational mechanics without framework abstraction obscuring core concepts. ## Core Programming Languages in the Curriculum According to the repository's [`AGENTS.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENTS.md) file, the codebase explicitly restricts implementations to four supported languages. Each serves a distinct pedagogical purpose within the structured learning path. ### Python: The Primary Teaching Language **Python** serves as the dominant instruction language throughout the repository, appearing in the majority of lesson implementations. The `phases/*/code/` directories contain numerous [`main.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/main.py) files that demonstrate everything from basic agent architectures to production LLM applications. Python handles the core teaching load because it provides readable syntax while maintaining sufficient performance for educational examples. The repository enforces strict stdlib-only Python code, prohibiting external dependencies that might hide implementation details from learners. ### TypeScript: Web Tooling and Site Generation **TypeScript** powers the repository's web infrastructure and build tooling. The [`site/build.js`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/site/build.js) file (compiled from TypeScript source) generates the static site that renders the curriculum materials. This language choice ensures that web-facing components remain type-safe while leveraging the vast JavaScript ecosystem for documentation generation. TypeScript implementations appear exclusively in tooling contexts rather than lesson implementations. ### Rust: Systems-Level Examples **Rust** appears in select lessons requiring memory safety or systems-level performance characteristics. The repository restricts Rust code to single-file examples compiled with `rustc --edition 2021`, avoiding complex Cargo workspaces that might distract from the AI engineering concepts being taught. These implementations typically appear in phases discussing performance optimization or low-level agent primitives, where Rust's ownership model provides explicit pedagogical value. ### Julia: Mathematical Computing **Julia** handles math-heavy lessons that rely on numerical computing and statistical operations. Implementations utilize the Julia standard library exclusively, specifically modules like `Random` and `Statistics`, to demonstrate algorithms without external ML frameworks. This language excels in lessons covering optimization algorithms, statistical methods, and mathematical foundations of machine learning where Python's dynamic typing might obscure numerical precision concepts. ## File Structure and Implementation Details The repository enforces its language policy through specific file naming conventions and directory structures. Each lesson resides in a numbered phase directory with a predictable layout. Key files demonstrating the multi-language architecture include: - **[`AGENTS.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENTS.md)** — Defines the allowed languages and the philosophical "stdlib-first" constraint that prohibits external dependencies - **[`phases/19-capstone-projects/87-end-to-end-safety-gate/code/main.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/19-capstone-projects/87-end-to-end-safety-gate/code/main.py)** — Illustrates a complete Python-based lesson entry point implementing safety gate patterns - **[`phases/08-multi-agent-and-swarms/04-primitive-model/code/main.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/08-multi-agent-and-swarms/04-primitive-model/code/main.py)** — Shows Python implementations of basic agent swarm architectures - **[`phases/11-llm-engineering/13-production-app/code/production_app.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/11-llm-engineering/13-production-app/code/production_app.py)** — Demonstrates production-grade LLM application patterns built from scratch - **[`site/build.js`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/site/build.js)** — The TypeScript-generated JavaScript responsible for static site generation ## Practical Code Examples The following snippets illustrate the coding style enforced throughout the curriculum for each supported language. These examples reflect the actual patterns found in lesson implementations. ### Python Entry Point Typical of the `phases/*/code/main.py` files found throughout the repository: ```python # main.py – a simple entry point for a lesson def main(): print("Hello, AI Engineering from Scratch!") if __name__ == "__main__": main() ``` ### TypeScript Build Utility Representative of the site-generation tooling: ```typescript // build.ts – a tiny TypeScript utility used by the site generator function greet(): void { console.log("Building the AI Engineering curriculum site..."); } greet(); ``` ### Rust Single-File Example A minimal binary adhering to the `rustc --edition 2021` constraint: ```rust // main.rs – a tiny Rust program (std-only) fn main() { println!("Hello from Rust in AI Engineering!"); } ``` ### Julia Mathematical Script Typical of numeric computing lessons utilizing the Julia standard library: ```julia # hello.jl – a basic Julia script println("Hello, Julia from AI Engineering!") ``` ## Summary The AI Engineering from Scratch repository employs a deliberate four-language strategy to teach AI concepts without framework dependency bloat: - **Python** provides the primary instructional language for AI and agent implementations across all phases - **TypeScript** handles documentation generation and web tooling through the [`site/build.js`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/site/build.js) generator - **Rust** offers systems-level examples compiled with edition 2021 for performance-critical lessons - **Julia** enables mathematical and statistical computing using only standard library modules - The [`AGENTS.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENTS.md) file enforces a strict "stdlib-first" policy preventing external dependencies in lesson code - Every lesson implementation follows the language specified in its documentation front-matter ## Frequently Asked Questions ### Is Python the only language used in AI Engineering from Scratch? No. While Python serves as the primary teaching language for most lessons in the `phases/*/code/` directories, the repository explicitly supports **TypeScript**, **Rust**, and **Julia** according to [`AGENTS.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENTS.md). TypeScript appears in build tooling like [`site/build.js`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/site/build.js), while Rust and Julia appear in specific lessons addressing systems programming and mathematical computing respectively. ### Why does the repository use multiple programming languages instead of just Python? The curriculum adopts a polyglot approach to match each lesson's pedagogical requirements with the most illustrative language. Python handles general AI engineering, Julia excels at mathematical foundations, Rust demonstrates memory-safe systems concepts, and TypeScript manages the documentation infrastructure. This diversity ensures learners understand language-agnostic principles rather than framework-specific implementations. ### What is the "stdlib-first" policy mentioned in AGENTS.md? The "stdlib-first" policy restricts all lesson code to standard library imports only, prohibiting external dependencies like NumPy, PyTorch, or external crates. This constraint forces implementations in `phases/*/code/main.py` and other lesson files to demonstrate algorithms from scratch, ensuring learners comprehend the underlying mechanics rather than calling abstracted library functions. ### Which programming language is best for production LLM apps according to this curriculum? The repository demonstrates production LLM applications primarily in **Python**, as evidenced by [`phases/11-llm-engineering/13-production-app/code/production_app.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/11-llm-engineering/13-production-app/code/production_app.py). While Python remains the practical choice for production AI engineering in the curriculum, the underlying concepts taught are language-agnostic, and the `stdlib-first` approach ensures the patterns translate to any production environment.