Practice · 2023–now · AI Present
Embed everything
If it is text, vectorize it. Semantic search cosplay for problems that needed a better filter.
Embed-everything failed as a default because embeddings without evals are expensive autocomplete. It stuck where semantic search has a clear metric. The costly fad was treating vectors as a substitute for information architecture.
Cost of the fad
Embedding pipelines for tickets, PDFs, Slack, and the cafeteria menu — then nobody measured retrieval quality. Vector bills and reindex jobs became the product.
Patterns
Context
The stack gets a co-pilot
AI pair programming is already changing how code is written. Agent frameworks and vector stores are still sorting winners from demos. The durable layer will look familiar: evals, retrieval quality, product UX, and ownership. Autopilot rewrites without tests are just big-bang migrations with better slides.
Compare with
Related
Platform · 2022–now
Vector DB gold rush
Specialized stores for embeddings. Many workloads are folding back into Postgres extensions — after the vendor tour.
Practice · 2023–now
RAG as default architecture
Retrieval-augmented generation as the answer to every knowledge problem. Often right; often Postgres with pgvector would suffice.
$ Teams stood up vector pipelines, chunking strategies, and rerankers before asking if fine-tuning or a SQL query would answer the question. Retrieval infra became the product.
Platform · 2023–now
Postgres + pgvector
Embeddings back in the database you already run. The quiet winner of the vector DB gold rush.