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Database Design for AI Applications
Prompt$81.30
Design database schemas and architectures that work well with AI workloads — including embeddings, audit trails, and LLM output storage.
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Description
AI applications have specific database needs: storing embeddings, conversation history, and auditing LLM outputs. This course covers relational, vector, document, and graph databases in the context of AI system design.
What’s Inside:
- AI Application Data Patterns
- Relational Design for AI
- Storing and Querying Embeddings
- Conversation History Schema
- LLM Output Storage Patterns
- Audit Log Design
- PostgreSQL with pgvector
- Document Stores for AI
- Redis for AI Caching
- Multi-Database Architectures
- Database Migrations for AI
- Project: Design a Complete AI App Schema
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