Hi everyone,

AI-powered applications depend on reliable, well-structured, and up-to-date data. However, building an effective AI data pipeline can be challenging when information comes from multiple sources, contains duplicates, or uses inconsistent formats.

I'm interested in learning how developers and data engineers manage data ingestion, preprocessing, transformation, and retrieval when building LLM-powered applications.

A few questions I'd like to discuss:

Which tools are useful for building scalable AI data engineering pipelines?

How can we improve data quality before indexing documents for a RAG application?

What are the best practices for chunking documents and generating embeddings?

How can developers evaluate retrieval accuracy and reduce irrelevant results?

Which approaches work best for keeping enterprise knowledge bases updated?

I'd appreciate hearing about your experiences, recommended tools, and practical approaches for building reliable AI data pipelines.