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A curated collection of 93+ tutorials and real-world project implementations covering LLMs, RAG systems, and AI agents, organized by difficulty level.
A curated collection of 93+ tutorials and real-world project implementations covering LLMs, RAG systems, and AI agents, organized by difficulty level.Is this a single project or a collection of separate tutorials?It is a collection: each entry is a standalone Jupyter Notebook project demonstrating a specific technique or tool, grouped into Beginner, Intermediate, and Advanced sections.
It is a collection: each entry is a standalone Jupyter Notebook project demonstrating a specific technique or tool, grouped into Beginner, Intermediate, and Advanced sections.Where should a beginner start?Beginners are pointed to the AI Engineering Roadmap first, then simple projects like OCR apps and basic RAG implementations before moving to agent-based intermediate projects.
Beginners are pointed to the AI Engineering Roadmap first, then simple projects like OCR apps and basic RAG implementations before moving to agent-based intermediate projects.Do all the projects need API keys or cloud models?No, many projects run fully local models like Llama 3.2, Gemma-3, DeepSeek-R1 and Qwen via Ollama, while others use cloud services like Groq, SambaNova, or NVIDIA NIM depending on the project.
No, many projects run fully local models like Llama 3.2, Gemma-3, DeepSeek-R1 and Qwen via Ollama, while others use cloud services like Groq, SambaNova, or NVIDIA NIM depending on the project.Is each project self-contained with its own instructions?Not explicitly detailed in the top-level README; each listed project likely has its own notebook and setup requirements that aren't described at the repo root level.
Not explicitly detailed in the top-level README; each listed project likely has its own notebook and setup requirements that aren't described at the repo root level.Who is this collection aimed at?It targets beginners, practitioners, and researchers in AI engineering who want hands-on, adaptable code examples rather than pure theory.
It targets beginners, practitioners, and researchers in AI engineering who want hands-on, adaptable code examples rather than pure theory.What topics does the MCP section cover?It includes projects like a custom MCP with deep web search, MCP-powered Agentic RAG for Cursor, MCP video RAG, a voice agent with Firecrawl and Supabase, and ML model management with KitOps.
It includes projects like a custom MCP with deep web search, MCP-powered Agentic RAG for Cursor, MCP video RAG, a voice agent with Firecrawl and Supabase, and ML model management with KitOps.Are there projects comparing different LLMs head-to-head?Yes, there's a Model Comparison section with projects pitting Llama 4 vs DeepSeek-R1, Qwen3 vs DeepSeek-R1, O3 vs Claude Code, and GPT-OSS vs Qwen3 on tasks like coding and reasoning.
Yes, there's a Model Comparison section with projects pitting Llama 4 vs DeepSeek-R1, Qwen3 vs DeepSeek-R1, O3 vs Claude Code, and GPT-OSS vs Qwen3 on tasks like coding and reasoning.What kind of advanced projects are included?Advanced projects cover fine-tuning models with Unsloth, building reasoning models like DeepSeek-R1, implementing Transformers from scratch, multi-agent deep researchers, and production systems like a full NotebookLM clone.
Advanced projects cover fine-tuning models with Unsloth, building reasoning models like DeepSeek-R1, implementing Transformers from scratch, multi-agent deep researchers, and production systems like a full NotebookLM clone.Is there a bonus resource for subscribing to the newsletter?Yes, subscribing to the project's newsletter gives a free Data Science eBook with over 150 lessons.
Yes, subscribing to the project's newsletter gives a free Data Science eBook with over 150 lessons.What are similar projects?ai-system-design, nlp_course, ai-engineering-interview-questions-company-wise, ai-engineer-notebooks, awesome-ai-engineering, llm_interview_note, AI-Engineering-Lab, llm-course
ai-system-design, nlp_course, ai-engineering-interview-questions-company-wise, ai-engineer-notebooks, awesome-ai-engineering, llm_interview_note, AI-Engineering-Lab, llm-courseWhich topics is it in?ai-engineering, retrieval-augmented-generation, llms, jupyter notebook, fine-tuning, large-language-models, nlp, ollama, prompt-engineering, chatbot, rag, multi-agent
ai-engineering, retrieval-augmented-generation, llms, jupyter notebook, fine-tuning, large-language-models, nlp, ollama, prompt-engineering, chatbot, rag, multi-agent