AI Fundamentals Resources
Use this page as the "go deeper" reference for the whole AI Fundamentals section - standards and papers behind the architecture, books and courses to build real depth, and the tools you'll actually touch.
Key Papers
| Paper | Relevance |
|---|---|
| Attention Is All You Need (Vaswani et al., 2017) | The original Transformer architecture paper - see LLM Architecture |
| Chain-of-Thought Prompting Elicits Reasoning (Wei et al., 2022) | Foundational reasoning-prompting technique - see Prompt Engineering |
| Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022) | Majority-vote sampling over multiple reasoning paths |
| ReAct: Synergizing Reasoning and Acting (Yao et al., 2022) | The reasoning-plus-action interleaving pattern underlying most agent loops - see Agentic AI Fundamentals |
| Lost in the Middle (Liu et al., 2023) | Position-dependent performance degradation in long contexts - see Context Engineering |
| Xiao & Zhu, Foundations of Large Language Models | Accessible coverage of pre-training, fine-tuning, prompting, and alignment |
Books
| Book | Author |
|---|---|
| Hands-On Large Language Models | Jay Alammar & Maarten Grootendorst - heavily illustrated, covers Transformer internals through RAG and fine-tuning |
| AI Engineering: Building Applications with Foundation Models | Chip Huyen - prompt engineering, RAG, fine-tuning, agents, and production serving tradeoffs |
| Designing Machine Learning Systems | Chip Huyen - the ML-systems fundamentals underneath any LLM-specific stack |
Courses
| Course | Provider |
|---|---|
| ChatGPT Prompt Engineering for Developers | DeepLearning.AI (Isa Fulford, Andrew Ng) |
| LangChain for LLM Application Development | DeepLearning.AI |
| Hugging Face NLP Course | Hugging Face - free, covers Transformers library end to end |
Tools
| Tool | Purpose |
|---|---|
| Hugging Face Transformers | The standard library for working with open-weight model architectures - see Open-Weight vs. Closed Models |
| LangChain | Framework for building LLM-powered applications and agents |
| LlamaIndex | Data-framework for connecting LLMs to retrieval/RAG pipelines - see RAG Architecture |
Blogs & Engineering Research
- Anthropic Engineering Blog - applied context engineering, agent harness design, tool-use patterns
- LangChain Blog - practitioner-level agent/context engineering writeups
- Chip Huyen's Blog - ML systems and AI engineering fundamentals
Where to Go Next on This Site
- Start from the top: AI Fundamentals Overview
- Apply it to security: AI Security Overview
- Already comfortable with architecture? Jump to LLM Security and Agentic AI Security directly