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Understand AI.
In depth.
Clear, deep articles on how AI actually works, the tools and tech stack behind it, and how to build with it — written so anyone can go from curious to expert.
What you'll find here
Three pillars of AI knowledge
AI Fundamentals
How neural networks, transformers and large language models really work — explained from first principles, no hype.
Read fundamentals →AI Tech Stack
Every layer of a modern AI system — models, data, vector databases, orchestration, serving and monitoring — and how they fit.
Explore the stack →AI Development
Hands-on deep dives with real code: build retrieval-augmented apps, agents and evaluation pipelines step by step.
Start building →Latest articles
Start reading
Long-form, in-depth guides. Each one stands on its own — thirteen and counting.
Responsible AI: Risks, Safety and Good Practice
Hallucinations, bias, privacy, security and misuse — the real risks, and the practical habits that prevent most problems.
FundamentalsMultimodal AI: Images, Audio and Beyond
How AI sees images, hears speech and creates pictures — vision encoders, CLIP, diffusion — and where it still stumbles.
Tech StackRunning AI Models Locally
Run open-weight models privately on your own computer: hardware, quantization, and setup with Ollama, LM Studio and llama.cpp.
FundamentalsThe AI Glossary: 60+ Terms Explained
Plain-English definitions of the AI terms you'll meet — from agents and attention to tokens and transformers.
DevelopmentBuilding AI Agents with Tool Use
Tool use, the agent loop, tool design and safety guardrails — with a working agent built from scratch in Python.
DevelopmentEvaluating LLM Applications
Test sets, code checks, LLM-as-judge, RAG and agent metrics — turn AI quality from guesswork into engineering.
The AI Tech Stack
See the whole picture
Modern AI products are built from layers. Understanding each layer — and how they connect — is what separates people who use AI from people who build with it.
Read the full breakdown- ApplicationsChatbots, copilots, agents
- OrchestrationPrompts, tools, agent frameworks
- Data & RetrievalEmbeddings, vector databases, RAG
- ModelsFoundation models, fine-tuning
- Serving & OpsInference, evals, monitoring
- ComputeGPUs, accelerators, cloud
Learning roadmap
Not sure where to start?
Follow this path. Each stage builds on the one before it.
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1
Understand
Learn what LLMs are and how they produce answers.
How LLMs Work → -
2
Map the landscape
See every layer of the AI stack and the tools in each.
The AI Tech Stack → -
3
Build
Learn embeddings, then build an AI app over your own data.
Build a RAG App → -
4
Go further
Build agents that use tools, and measure quality with evals.
Build AI Agents →
FAQ
Questions, answered
Is everything really free?
Yes. Every article is free to read, with no account, paywall or sign-up required.
Do I need a technical background?
No. Fundamentals articles assume no prior knowledge. Development articles include code, but explain each step so you can follow along even if you're new to programming.
I keep seeing AI jargon I don't understand. Where do I look it up?
Our AI Glossary explains 60+ common terms in plain English, with links to in-depth articles.
How often is new content added?
New articles are added regularly, and existing ones are updated as the AI field evolves.
Can I suggest a topic?
Please do — email hello@beaiexpert.com with what you'd like explained.
Be the AI expert in the room.
Start with one article. Understand it deeply. Then the next. It's all free.