Editorial desk
RAGStackGuide Editorial
The editorial desk that publishes RAGStackGuide. It is a byline for the site's editorial process, not a person, and this page carries no author biography because there is no individual author to describe.
How this desk works
- Articles are researched from primary sources: vendor and project documentation, published standards and specifications, release notes, advisories, and measurements published by the people who took them.
- Drafts are produced with AI assistance and then edited against those same sources before publication.
- Nothing published here claims hands-on lab testing, benchmarking, or first-hand measurement. Where a figure comes from a datasheet or someone else's test, the article names the source.
- Corrections go to hello@ragstackguide.com and are made on the affected page. Funding is set out on the disclosure page.
Posts (6)
- embeddings
Best Embedding Model for RAG: How to Choose in 2026
The best embedding model for RAG depends on your corpus. A 2026 shortlist: Voyage, Gemini, Cohere, Qwen3, BGE-M3, plus a bake-off script to rank them.
- rag
RAG vs Fine-Tuning Explained: A Production Decision Guide
Choose RAG, fine-tuning, both, or neither by comparing knowledge freshness, quality, p99 latency, retrieval drift, training cost, and deploy risk.
- vector-databases
Qdrant vs Milvus vs Pinecone: Vector DB Comparison
Compares Qdrant, Milvus and Pinecone on deployment, indexing, filtering, hybrid search and operations to find the right vector database for a RAG workload.
- troubleshooting
RAG Retrieval Debugging: Why Results Come Back Wrong
A stage-by-stage checklist for RAG retrieval failures: isolate the fault to extraction, chunking, embedding, filtering or ranking before changing anything.
- pipeline-design
RAG Pipeline Architecture: Components and Build Order
The seven components of a RAG pipeline, what each one decides, and the order to build them in so retrieval quality is measurable before you tune anything.
- retrieval-quality
RAG Chunking Strategy: Picking Chunk Size and Overlap
Explains how to choose RAG chunk size and overlap, split on document structure, add retrieval metadata and measure recall before tuning.