Databases for AI Apps implementation checklist

Verify that the vector column dimensions (e.g., 1536 for OpenAI) exactly match the output of the embedding model to prevent runtime query failures.

Apply HNSW (Hierarchical Navigable Small World) or IVFFlat indexes to embedding columns; avoid sequential scans for datasets exceeding 10,000 rows.

Confirm the database distance function (cosine, L2, or inner product) matches the metric the embedding model was trained on.

Ensure the database instance RAM is large enough to hold the entire vector index (e.g., HNSW) to avoid disk swapping during similarity searches.

Adjust m and ef_construction parameters for HNSW to balance build time versus search recall based on your specific latency requirements.

Deploy PgBouncer or use a platform-native pooler (e.g., Supabase, Neon) to manage connection overhead from serverless functions.

Set a strict statement_timeout (e.g., 30s) to prevent long-running AI queries or vector index builds from exhausting the connection pool.

Configure ORM connection strings for transaction mode if using a pooler to prevent prepared statement errors in serverless environments.

Verify that database drivers use HTTP or WebSocket protocols if deploying to edge runtimes like Cloudflare Workers or Vercel Edge.

Configure the pooler to close idle connections after 60 seconds to accommodate the bursty nature of LLM application traffic.

Include a 'model_version' column in tables storing vectors to facilitate re-indexing if you migrate from one embedding model to another.

Apply GIN indexes to JSONB columns used for filtering vector results (e.g., user_id, organization_id) to ensure hybrid search performance.

Implement a partitioning or cleanup strategy for chat history tables to prevent unbounded storage growth and query degradation.

Enforce foreign key constraints between embedding chunks and their source documents to prevent orphaned vectors in RAG systems.

Enable row-level logging for tables containing sensitive context data used in AI prompts to meet compliance requirements.

Increase max_parallel_workers_per_gather to allow the database to use multiple CPU cores for large-scale vector similarity scans.

Implement batching for vector inserts (e.g., 100-500 rows per transaction) to minimize WAL (Write-Ahead Log) overhead during data ingestion.

Route heavy RAG retrieval queries to read replicas to ensure embedding generation and chat history writes remain performant on the primary.

Run EXPLAIN ANALYZE on common vector queries to confirm that indexes are being utilized rather than full table scans.

Test scalar or product quantization if vector storage costs exceed budget or if index size exceeds available RAM.

Enable and test RLS policies to ensure that vector similarity searches only return data the requesting user is authorized to view.

Validate that the ingestion pipeline scrubs PII from text before it is stored in the database or sent to embedding providers.

Establish VPC peering or PrivateLink between the application backend and the database to prevent exposure of sensitive AI data to the public internet.

Verify that the database provider has AES-256 encryption enabled for all storage volumes and automated backups.

Create a dedicated database user for the AI service with permissions restricted to specific schemas and tables.