Page 3 of AI Engineering
A reproducible LlamaIndex pipeline that sweeps chunk sizes against latency, faithfulness, and relevancy using GPT-4 as an LLM judge, so RAG teams can ship chunk configurations backed by evidence.
A practical look at how Apache Arrow's zero-copy, memory-mapped storage lets Hugging Face Datasets handle multi-GB datasets on ordinary laptops, and when to choose Dataset over IterableDataset.
A practical guide to choosing between Keras preprocessing layers and tf.image-based augmentation, and between offline pre-generation and online on-the-fly execution, based on dataset size, compute budget, and reproducibility requirements.
A diagnostic guide that maps three recurring failures of system prompts — conflicting signals, attention scarcity, and state erosion — to the structural fixes that resolve each one.
A practitioner's guide to using a knowledge graph as durable agent memory, with Graphiti's EntityNode and episode pipeline as the concrete read/write loop.
A practical breakdown of the n8n AI Starter Kit's five Docker services, how n8n orchestrates while MCP processes AI tasks, and how to pick a hosting environment from laptop to datacenter.
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…