Learn AI
Build your understanding from AI fundamentals to production AI systems.
Prefer a guided sequence? Explore Learning PathsAI Engineering
Learn the engineering practices required to build reliable AI applications.
10 conceptsAI Application Architecture
AI application architecture is the overall structure connecting a frontend, backend, and model provider into a working product.
Intermediate · 5 minLLM Gateway
An LLM gateway is a shared layer that routes requests to model providers while handling auth, logging, and fallback.
Intermediate · 4 minStreaming
Streaming returns a model's output incrementally as it's generated instead of waiting for the full response.
Beginner · 3 minCaching
Caching stores previous results so repeated or similar requests can be served faster and more cheaply.
Beginner · 3 minRate Limiting
Rate limiting restricts how many requests a client can make in a given time window to protect a system from overload.
Beginner · 3 minObservability
Observability is the ability to see what an AI system is doing in production through logs, traces, and metrics.
Intermediate · 4 minEvaluation
Evaluation systematically measures the quality of a model's or system's outputs against defined criteria.
Intermediate · 4 minGuardrails
Guardrails are checks that constrain model input or output to prevent unsafe, incorrect, or off-policy behavior.
Intermediate · 4 minCost Optimization
Cost optimization reduces the expense of running AI systems through techniques like caching, routing, and smaller models where appropriate.
Intermediate · 4 minAI Security
AI security protects AI systems from risks such as prompt injection, data leakage, and misuse of tools.
Advanced · 5 min