AI
Understand how large language models work, how to build with them, and how to ship AI features safely.
How LLMs Actually Work
At their core, large language models are next-token predictors. Transformers, self-attention, and why they hallucinate.
Tokens, Embeddings, and Context Windows
The three concepts that explain most practical LLM behavior. How text becomes numbers, what those numbers mean, and why there's a hard limit on how much the model can see.
Prompt Engineering
Not magic words — clear specification. Core techniques for getting reliable, useful output from language models.
RAG — Retrieval Augmented Generation
The model's knowledge is frozen at training time and knows nothing about your data. RAG fixes both by retrieving relevant documents and stuffing them into the prompt.
Fine Tuning vs RAG
The most common architectural decision in LLM apps, and the clean rule that simplifies it: RAG for knowledge, fine-tuning for behavior.
AI Agents — How They Work
An agent is an LLM in a loop with tools and a goal. How the observe-reason-act cycle, tool calling, planning, and memory combine — and where it all breaks down.
Vector Databases
The storage layer RAG depends on. How vector databases index and search high-dimensional embeddings at scale using approximate nearest neighbor algorithms.
LLM APIs — OpenAI, Anthropic, Gemini
The practical mechanics of calling LLM APIs. Messages, streaming, tool calling, structured outputs, token accounting, rate limits, and key management across providers.
AI in Production — Costs and Scaling
The gap between a working demo and a system you can afford to run. Cost optimization, latency management, reliability patterns, evaluation, and observability for LLM applications.
AI Security Risks
Threat modeling your LLM application. Trust boundaries, OWASP LLM Top 10 as a builder's checklist, and defense in depth for AI systems.
Prompt Injection Attacks
The defining unsolved vulnerability of LLM applications. How prompt injection works, why it's fundamentally hard to fix, and what defenses actually help.
Building Secure AI Products
A reference architecture for secure LLM applications. Layered defenses across input, context, model, output, and action stages — with authorization at the tool boundary, not in the prompt.