Practical guides on building with large language models, from model APIs and prompt engineering to agents, RAG systems, and evaluation. Learn how real teams design, build, and ship production AI features with clean, maintainable code. Covers the tools, patterns, and architectures modern AI engineers rely on every day.
A pragmatic guide to choosing a Creative Commons or Open Data Commons license for a first Hugging Face dataset, grounded in EU publisher rights and attribution conventions.
An operational comparison of recursive, semantic, section-aware, and late chunking for RAG ingestion, with guidance on which method fits which document type.
A practical buyer's guide that compares Caveman variants against external alternatives, helping readers pick a compressed-output skill based on workflow, model, and stack rather than headline token-savings numbers.
Why pure vector search misses exact product codes and names, and how to assemble the three-stage production baseline: parallel BM25 and dense candidate generation, Reciprocal Rank Fusion to merge the ranked lists, and a cross-encoder reranker for final ordering — implemented with Qdrant and Cohere.
Shipping one RAG deployment to many organizations forces a hard question: one shared Qdrant collection with tenant filters, or a separate collection per customer? This guide compares both isolation strategies, explains how Qdrant's filterable HNSW keeps tenant data fast and separated, and maps how…
Ragas doesn't just write questions — it builds a knowledge graph from your documents and then evolves simple prompts into reasoning, conditioning, and multi-context challenges. Here is what the generator LLM, the critic LLM, and the embedding model are actually doing inside testset generation, and…
VRAM — not clock speed — decides which models your machine can actually run, and that single fact reshapes how every dollar should be spent. This guide walks through GPU tiers, the CPU's underrated role, and multi-GPU decisions with real 2026 street prices and benchmarks, so your local AI…
Running vllm serve doesn't start one program — it spawns a tree of API servers, engine cores, GPU workers, and sometimes a DP Coordinator, all wired together with ZMQ sockets. This walkthrough traces vLLM's V1 multi-process architecture from the CLI to the first forward pass on the device, step by…
Most engineers obsess over chunking strategies and vector databases, yet the pipeline that feeds them is where the real engineering lives. This guide maps all 11 stages of an automated docs ingestion pipeline — sitemap URL discovery, HTML cleaning, embedding, vector upserts, and incremental refresh…
When you call a model through LiteLLM, the model string you send is just a public name for a pool of deployments — and the Router decides which one actually answers. This deep dive traces that decision end to end: model_name matching, multi-provider model groups, routing groups vs. access groups…
From Claude Code's 1M-token terminal refactors to Devin's fully delegated pull requests, we ranked the ten best AI coding agents of 2026 on real benchmark scores, pricing, and autonomy. Here's which agent fits your workflow — and which failure modes the demos never show you.