Netflix's AI Search: From DSL to Natural Language

Unpacking Netflix's AI Search Engine

This article provides a compelling look into Netflix's implementation of natural language search using LLMs and graph databases. The use of Retrieval-Augmented Generation (RAG) to handle the complexity of the data schema is particularly innovative. The detailed explanation of context engineering, including field RAG and controlled vocabulary RAG, is valuable for developers tackling similar challenges. The focus on syntactic and semantic correctness, with validation steps, demonstrates a practical approach to building robust AI-powered search systems. However, the article could benefit from expanding on the performance metrics and the specific LLMs used, as well as the challenges faced in production. While the article highlights pragmatic correctness, it doesn't delve deeply into the methods used to improve it, which is crucial for overall user satisfaction. Furthermore, a discussion on the cost (both financial and computational) of running such a system would be useful.

Key Points

• Netflix is moving from structured query languages to natural language search using LLMs.
• They are employing Retrieval-Augmented Generation (RAG) to handle the complexity of their data schema.
• Context engineering, including Field RAG and Controlled Vocabulary RAG, is a key component.
• They focus on syntactic, semantic, and pragmatic correctness, with validation steps to ensure reliability.
• The system aims to augment existing applications by providing natural language search capabilities, not to replace them.

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📖 Source: The AI Evolution of Graph Search at Netflix