The Embedding
Knowledge Base
Guides, best practices, and deep dives on managing vector data safely at scale. Everything you need to ship retrieval with confidence.
Detecting Embedding Drift: The Silent Killer of RAG Accuracy
Your RAG pipeline shipped fine. Then answers started slipping. The problem is upstream, not the LLM. Here's how embedding drift breaks retrieval and what to do about it.
I Updated My Embedding Model and My RAG Broke: A Post-Mortem
Upgrading from text-embedding-ada-002 to text-embedding-3-small looks simple, until your search results turn to garbage. Here's why embedding model migrations silently break RAG, and how to do them safely.
Why Your Pinecone Index Keeps Breaking (and the Vector Ops Fix)
You have CI/CD for your frontend, backend, and infrastructure. Why is your AI data still a manual upsert-and-pray process? Introducing Vector Ops: deployments for your vector database.
MTEB Won't Tell You Which Embedding Model to Use
Leaderboard scores measure general performance on general data. Your corpus isn't general. Here's how to actually pick an embedding model: what the real variables are, when task type matters more than model choice, and how to measure it on your own documents.
Why Your RAG Got Worse After Switching Embedding Models (And How to Fix It)
Switching embedding models rewrites your entire vector space. A model that benchmarks better on MTEB may retrieve worse on your documents. Here's how to diagnose what went wrong and run a controlled comparison before your next re-embed.
How to Design a Reusable RAG Pipeline (Without Rewriting Everything)
Hardcoding chunking, embedding, and retrieval into a single function means every config change is a code change. Here's the strategy abstraction that fixes it: separate configuration from execution, test configs independently, and save the ones that work.
How to Actually Choose the Best Embedding Model for Your RAG
Most teams pick an embedding model by reading benchmarks and guessing. Here's the exact process to find the right one for your data: 15 documents, an auto-generated gold set, four strategies in parallel, and results in under an hour for less than $0.05.
Stop Embedding Your Entire Corpus Blindly
Most teams pick an embedding model, chunk arbitrarily, embed everything, and hope. That loop costs real money every time it breaks. Here's why sample-first RAG design is the only rational way to stop paying for failed experiments.