Concepts Guides
A curated collection of 8 concepts guides for building with AI coding agents.
Which Agent Framework in 2026? LangGraph vs CrewAI vs AutoGen vs OpenAI Agents SDK vs Claude Agent SDK
A decision guide to the major AI agent frameworks — control vs. abstraction, multi-agent models, state and durability, and which fits your project.
Agent Memory Architecture: Short-Term, Long-Term, and When to Use Each
How AI agents remember — working memory vs. persistent long-term memory, what to store, how to retrieve it, and how to keep context small.
Calling Any Model: Unified LLM Gateways & SDKs in 2026
Why teams put a unified layer in front of LLM providers — and how LiteLLM, OpenRouter, and the Vercel AI SDK compare for fallback and cost control.
Choosing Embeddings in 2026: OpenAI vs Cohere vs Voyage vs Open-Source
A decision guide for picking an embedding model for retrieval — accuracy, dimensions, cost, multilingual and domain fit, self-hosting, and lock-in.
How RAG Actually Works: Ingestion, Chunking, Retrieval & Reranking
A clear, practical walkthrough of the retrieval-augmented generation pipeline — what each stage does, where it fails, and how the pieces fit together.
Hybrid Search & Reranking: From Top-50 Recall to Top-5 Precision
How production RAG combines dense and sparse search, fuses with RRF, and reranks — turning a wide candidate set into the few passages that actually answer.
Production Tool & Function Calling: Feed Errors Back as Observations
How agents use tools — the call/observe/retry loop, why errors must return to the model, and the schemas, idempotency, and limits that keep it reliable.
Structured Output vs JSON Mode vs Function Calling: Which to Use in 2026
The reliable ways to get typed data out of an LLM — what JSON mode, function calling, and native structured outputs each guarantee, and when to use which.