What does AI-ready data mean?
AI-ready data is data an AI system can train on, retrieve from or be evaluated against without a project-specific rescue effort: known provenance, measured quality, an access policy, a documented owner, and a shape a model can consume, whether that is a feature table, a chunked and embedded document store or a labelled evaluation set.
It is a property of the platform, not of a single dataset. When every new use case starts by rebuilding its own pipeline, the data is not AI-ready however clean each copy is.
Does every GenAI application need a vector database?
No. A vector database is needed when an application must find semantically similar content across a corpus too large to fit in a prompt. Many GenAI applications work over a small, known set of documents, a structured database or an API, and need no vector store at all.
When one is needed, the choice between a dedicated vector database and vector capabilities inside an existing database is decided by scale, latency, filtering needs and what the team can operate.
Vector database vs traditional database?
A traditional database answers exact questions: this customer, this order, this date. A vector database answers similarity questions: passages that mean roughly this. Production RAG systems usually need both, with metadata filters from the first constraining the similarity search of the second.
Most relational and document databases now offer vector indexes. For moderate corpora that is often the simpler operational choice.
What is hybrid search?
Hybrid search combines keyword (lexical) retrieval with semantic (vector) retrieval and merges the results, usually with a re-ranking step. It exists because vectors miss exact identifiers, part numbers and rare terms, and keywords miss paraphrase and meaning.
For enterprise content full of codes, names and acronyms, hybrid retrieval is the default we start from, then measure.
When does an AI system need a knowledge graph?
When the questions depend on relationships between entities, such as which supplier feeds which plant that ships to which customer, and those relationships are not recoverable from text similarity alone. If the questions are about finding relevant passages, a graph is unnecessary overhead.
We treat a knowledge graph as an addition justified by a measured retrieval gap, not as a starting architecture.
What data is required for RAG?
A curated corpus with access control that mirrors the source systems, metadata rich enough to filter on, a chunking strategy that respects document structure, and an evaluation set of real questions with known good answers. The evaluation set is the part most teams forget and the part that makes everything else measurable.
Freshness matters too: a pipeline that re-indexes changed documents on a known cadence, with the old versions retired.
How do you evaluate retrieval quality?
With an evaluation set of questions and the passages that should answer them, scored for precision, recall and rank, and re-run after every change to chunking, embeddings, filters or index configuration. Answer quality is measured separately so a retrieval regression is not mistaken for a model problem.
Production queries are then sampled and reviewed to keep the evaluation set honest.
How retrieval fits into end-to-end AI evaluation
How should unstructured enterprise data be prepared for AI?
By classifying it first, then extracting, cleaning and structuring it in a pipeline that records provenance, preserves access rights, handles tables and images deliberately, chunks by document structure rather than fixed character counts, and versions the result so an index can be rebuilt reproducibly.
Preparation is a data product like any other: owned, tested and monitored, not a one-off script run before the demo.