I've come to prefer the opposite order. Write the documentation first — or at least alongside the implementation — and let the act of explaining the feature shape the feature itself.
The fastest way to find out that an API is awkward is to try to document it. If the "getting started" example needs three paragraphs of caveats before the first line of code runs, that's not a docs problem — it's a design problem the docs just surfaced.
When you write the docs early, you're forced to answer the questions a real user will ask on day one:
Answering those questions before the code is frozen means you can still act on the answers. That's the whole point.
A code sample in your docs is a promise: run this and it works. The problem is that promises rot. A parameter gets renamed, a default changes, a method moves — and the example that was correct at launch quietly becomes a trap for the next person who copies it.
The fix is to stop treating documentation examples as prose and start treating them as tests. Every runnable snippet should be executed against the current release in CI. If the snippet breaks, the build breaks, and you find out before your users do. The same goes for the factual claims around the code: parameter names, defaults, and types can be checked against the source instead of trusted to stay in sync by hand.
Once your examples are validated automatically, documentation stops being the thing that's always slightly out of date and becomes the most trustworthy description of how the product actually behaves.
You don't need to flip your whole process overnight. A lightweight version:
The payoff isn't just better docs. It's a tighter feedback loop between the people building the product and the people using it — which, in the end, is what developer experience is really about.
]]>Getting a handful of objects into a vector database is easy. Getting millions in — reliably, without silently dropping data or melting your throughput — is where most ingestion pipelines start to hurt.
This guide walks through the patterns that hold up at scale:
blobHash type for storing media efficiently, without standing up OCR pipelines or extra infrastructure.A coding assistant is only as good as the context you can feed it. This walkthrough builds one that retrieves over your own code and documentation using Weaviate's built-in MCP server.
It covers the pieces that make retrieval over a codebase actually work:
This is a repost. Read the original article →
Social networks are a natural fit for graph data: people are nodes, relationships are edges, and the questions you actually care about — who's central, which communities form, how information spreads — are questions about structure.
This walkthrough builds an end-to-end app for exploring one:
The result is a live, explorable picture of a social graph — the kind of visualization that makes structure obvious in a way a table never will.
]]>Hybrid search combines vector similarity with exact keyword matching — and the keyword half lives or dies by tokenization: how text gets broken into the discrete units that BM25 scores against.
The article gets concrete about the choices that matter:
/v1/tokenize endpoint — test how an analyzer configuration behaves before committing to it, without reindexing your data."Add security later" is how most AI prototypes ship — and how they get stuck before production. This piece follows a fictional health-tech company from a single API key all the way to a compliance-ready setup.
Along the way it covers the building blocks of enterprise-grade security in Weaviate:
Authentication and authorization get lumped together, but they answer different questions: who are you? and what are you allowed to do? Getting the second one right is what keeps a shared database from becoming a liability.
The guide walks through both sides in Weaviate: