Caching Economics: Building Cost-Efficient LLM Pipelines
Learn how prompt caching cuts API bills by up to 90% and evaluate your token economics live using our interactive calculator.
Deep architectural specs, production boilerplates, and systems-level tutorials for full-stack AI engineers. Learn how to scale RAG, build reliable agent harnesses, and optimize LLM costs.
Master systems-level design patterns for models, tools, memory, evaluation harnesses, and security boundaries. Outlines 46 chapters of conceptual depth.
Build one cohesive customer agent using React, FastAPI, LangGraph, pgvector, and Redis. Includes step-by-step code annotations.
Scale to millions of requests with Kafka document ingestion, Temporal durable workflows, Kubernetes, and distributed tracing.
Explore comprehensive engineering roadmaps split by technology stack and core system domains.
Learn Vercel AI SDK patterns, dynamic Generative UI integrations, streaming components, and prompt interfaces.
Master FastAPI routers, server-sent events (SSE), tool/function calling schemas, and guardrail interception pipes.
Build cyclical graphs in LangGraph, define multi-agent teams, coordinate states, and inject Human-in-the-loop checkpoints.
Optimize pgvector indexes, handle semantic cache hits with Redis, manage queue architectures, and scale containers.
Explore production-ready repositories containing complete, runnable code templates.
A multi-agent customer ticket router that reviews requests, calls lookup APIs, and escalates unresolved paths to human channels.
A Next.js 15 chatbot client that renders custom React widgets dynamically on model tool calls, streaming messages over SSE.