An AI application does not repair misunderstood data. It may make the error more convincing by turning it into a fluent answer. Data architecture for AI is therefore less about selecting a vector database than organising provenance, access, quality, evaluation and operations.
Start with the expected answer
Define the decision or action supported by the system before designing a pipeline. Internal document search has different requirements from customer support or resource allocation.
For every answer, determine which sources are authoritative, how fresh they must be, which citations are visible, who may access the document, how an insufficient answer is recognised and how the source is corrected.
This prevents teams from pouring documents into an index without defining "correct".
Build a traceable chain
A typical RAG chain includes collection, extraction, cleaning, chunking, enrichment, indexing, retrieval and generation. Every stage should preserve an identifier linking each fragment to its document, version and permissions.
Chunking strongly affects quality. Short fragments lose context; long fragments dilute signal and increase cost. Test strategies against real questions instead of selecting a universal size.
Metadata matters as much as embeddings. Document type, product, language, date, owner, confidentiality and validity enable filtering before retrieval.
Vector database or existing database
A specialised vector database becomes useful when volume, index types, distribution or latency justify it. For an initial system, an existing relational database with vector support may simplify backup, permissions and operations.
Compare vector volume, update frequency, structured filters, consistency, restoration, available skills and migration cost. Our RAG quality and vector choice guide covers these trade-offs. Do not create a second source of truth: the index serves retrieval while original systems remain responsible for content.
Measure quality at several levels
A green pipeline does not guarantee a useful answer. Measure:
- ingestion completeness, errors and delay;
- retrieval of expected passages;
- grounded generation and output format;
- task resolution and user outcome;
- data leakage, stale content and unsupported answers.
Create a question set with expected sources. Include unanswerable questions, different permissions and conflicting documents. The system must be able to say it does not know.
Our article on data pipeline observability covers technical signals, while data quality for generative AI addresses business meaning.
Manage freshness, deletion and permissions
A document update should trigger reindexing or make the old version unavailable. Deletion must propagate to fragments, caches, indexes and evaluation sets where policy requires it.
Apply permissions before passages enter model context. Hiding an answer after generation is too late. Explicitly test that users cannot retrieve documents they are not allowed to see.
Assign an owner and validity period to critical datasets. Unowned data eventually becomes a dependency that nobody dares to fix.
Operate it as a service
Set freshness, availability and latency objectives. Monitor cost per request, context volume, ingestion failures, result drift and unsupported-answer rates.
Prepare degraded modes: conventional search without generation, answers limited to verified documents or human handoff. Model unavailability should not necessarily make all knowledge inaccessible.
Model, embedding and chunking changes require comparison on the same evaluation set. Our production MLOps monitoring guide provides a continuity framework.
Build progressively
Start with one understood source, an observable pipeline, one index and fifty evaluated questions. Add sources, formats and automation later. This exposes real problems before a complex platform hides them.
A strong data architecture can explain where an answer came from, who was allowed to see it, when its source changed and how the system was tested.
