Concepts Guides
Explore 16 curated concepts guides for building with AI coding agents — what each one does, when to use it, and how to add it to your workflow.
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.
Agentic RAG: When Retrieval Needs an Agent in the Loop
What agentic RAG is — retrieval as a tool an agent uses iteratively, with query planning, self-correction, and multi-source routing — and when the upgrade pays.
AI Coding Statistics 2026: The Numbers That Are Actually Sourced
How much code AI writes, who uses the tools, and what it does to quality — every statistic dated and traced to its primary source, updated on a cadence.
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.
GraphRAG Explained: When Knowledge Graphs Beat Vector Search
What GraphRAG is, how graph-based retrieval differs from vector RAG, the query shapes where it wins, and the honest costs before you build one.
How Computer-Use Agents Work
Inside the perception-action loop that lets AI operate real software — screenshots in, clicks out — plus grounding, reliability, and when to use APIs instead.
How Embeddings Work: Vectors, Similarity, and Choosing a Model
What an embedding actually is, how similarity is measured, how the models are trained, and the practical rules for using embeddings well in search and RAG.
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 Model Routing: Cut Cost Without Hiding Regressions
Design an LLM model router with capability gates, difficulty signals, cascades, fallbacks, per-route evals, shadow traffic, budgets, and safe rollout.
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.
RAG vs Long Context: Do Million-Token Windows Kill Retrieval?
Million-token context windows promised the end of RAG. The honest 2026 answer: long context changed where retrieval starts paying, not whether it does.
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.
Getting Web Data into AI Agents: Search & Scraping APIs Compared
The agent web-data layer — Exa for semantic search, Firecrawl for extraction at scale, Tavily for all-in-one, Jina Reader for zero-setup — and how they compose.