Your Vector Database Is a Search Index. Weaviate Is an AI Knowledge Base.
The first wave of vector databases focused on a single problem: fast and scalable similarity search. This was a critical first step, but it is not the end game. Weaviate represents the next step in the evolution of AI-native data infrastructure. It is an open-source vector database designed not just for retrieval, but for building dynamic, stateful AI applications that can learn and reason.
With its built-in modules for vectorization and generative models (e.g., Rerankers, Q&A), and its GraphQL API that treats data and vectors as a unified graph, Weaviate enables developers to build sophisticated Retrieval-Augmented Generation (RAG) and generative feedback loops directly at the database layer. But this power requires a developer to think like an AI application architect, not just a database user. An engineer who simply dumps vectors into Weaviate is using a supercar to drive to the grocery store; they are missing the point entirely.
This playbook explains how Axiom Cortex finds the developers who can harness the full power of Weaviate's AI-native architecture.
Traditional Vetting and Vendor Limitations
A vendor who can vet for Weaviate expertise is exceptionally rare. Most see it as just another vector database and test for basic knowledge of vector search. This completely fails to assess the skills needed to use Weaviate's more advanced, differentiating features.
The result of this superficial vetting is an application that fails to leverage Weaviate's unique strengths:
- Ignoring Generative Search: The team builds a complex, multi-step RAG pipeline in their application code, complete with separate calls to a reranker and a generative model, completely unaware that Weaviate's `generative-openai` or `reranker-cohere` modules can do this in a single, optimized database query.
- Schema Mismanagement: The developer fails to design a proper Weaviate schema with classes and properties, leading to inefficient filtering and an inability to use the GraphQL API to traverse relationships between objects.
- Inefficient Vectorization: The team builds a separate microservice just to vectorize their data before ingesting it into Weaviate, not realizing that Weaviate can do this automatically at ingestion time using a vectorization module like `text2vec-openai`.
How Axiom Cortex Evaluates Weaviate Developers
Axiom Cortex is designed to find engineers who think in terms of AI-native data systems. We test for the practical skills in schema design, advanced querying, and operational management that are essential for building production applications with Weaviate. We evaluate candidates across three critical dimensions.
Dimension 1: Weaviate Schema and Data Modeling
This dimension tests a candidate's ability to model their data in a way that unlocks Weaviate's full potential.
We provide a use case and evaluate their ability to:
- Design a Weaviate Schema: Can they design a clear schema with classes, properties, and cross-references to model the relationships in the data?
- Configure Vectorization and Modules: Can they choose and configure the appropriate vectorizer module for a class? Do they understand how to enable and configure other modules, like a reranker or a generative model?
Dimension 2: Advanced Querying with GraphQL
Weaviate's power is exposed through its GraphQL API. This dimension tests a candidate's fluency in writing sophisticated Weaviate queries.
We present a search problem and evaluate if they can:
- Perform Hybrid Search: Can they write a query that combines keyword-based (BM25) and vector-based search to get the best of both worlds?
- Use Generative Search: Can they write a query that uses a generative module to synthesize an answer from the retrieved documents, all within a single API call to Weaviate?
- Traverse the Graph: Can they use Weaviate's GraphQL syntax to traverse cross-references and answer complex questions about the relationships between data objects?
Dimension 3: Operations and Scalability
An elite Weaviate developer understands how to run and scale it in production.
We evaluate their knowledge of:
- Data Ingestion: Are they familiar with Weaviate's batching API for importing data efficiently?
- Replication and Sharding: Can they explain how Weaviate provides high availability and scales out for large datasets?
From a Vector Index to an AI Co-processor
When you staff your AI team with engineers who have passed the Weaviate Axiom Cortex assessment, you are investing in a team that can build truly next-generation AI applications. They will not just use Weaviate as a passive vector store; they will use it as an active component of their AI system, offloading complex RAG and generative logic to the data layer where it can be executed efficiently and at scale.