Structure the Unstructured: How JSON Columns Handle Non-Deterministic LLM Outputs

The text explores the challenges and solutions of managing non-deterministic outputs from AI systems, particularly those using large language models (LLMs). It critiques the common practice of using unstructured storage like NoSQL databases for AI-generated data, arguing that such outputs, despite their variability, possess inherent structure. The text introduces Pixeltable's JSON columns as a solution that combines the flexibility of storing arbitrary JSON structures with the advantages of structured, queryable, and versioned data tables. By utilizing JSON columns, developers can efficiently handle variable AI outputs, such as tool calls and extracted entities, without sacrificing queryability or the need to build custom infrastructures. It emphasizes the benefits of structured storage with JSON support, such as incremental computation, versioning, and cross-modal capabilities, and advises against treating AI outputs as completely unstructured. The text also outlines best practices for integrating JSON columns into existing workflows and the advantages of this approach over traditional document stores or graph databases.

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