#Rag

A practical guide to Google's EmbeddingGemma: matryoshka text embeddings designed for phones, Raspberry Pi, and edge devices—plus code examples, benchmarks, and build tips.

Learn how to build production-ready RAG systems with reliable retrieval, solid evaluation, smarter chunking, hybrid search, reranking, and fine-tuning—plus actionable patterns to ship with confidence.

Most enterprise data lives in PDFs, DOCX, and HTML. Here’s how Docling turns messy, unstructured files into clean, AI-ready context that improves RAG accuracy and reliability.

Build and ship internal knowledge and document-extraction apps in days, not months. A modular, Kubernetes-native approach with sandboxed iteration, LLM strategy, and a secure app factory.

Learn how RAG, fine-tuning, and prompt engineering improve LLM answers. See how they work, trade-offs, and practical steps to pick, combine, and implement them.