Author(s): Jeremy Arancio

Originally published on Towards AI.

A walkthrough to architect scalable and maintainable document indexing pipelines for RAG systems with Databricks Asset Bundles

Since the release of ChatGPT, companies have discovered that if you provide entreprise knowledge into prompts, LLMs were capable of answering requests with higher accuracy while preventing hallucinations.

This article discusses the concept of Retrieval Augmented Generation (RAG), the challenges encountered when building AI applications, and the necessity of establishing effective data ingestion pipelines. It aims to teach readers how to create production-ready pipelines that are maintainable and scalable by utilizing Databricks Asset Bundles. The article emphasizes the importance of planning, building, and deploying a robust data processing architecture to ensure that data pipelines function properly and remain manageable in an enterprise environment, while also outlining specific methods for versioning, implementing testing, and ensuring adaptability to changes over time.

Read the full blog for free on Medium.

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Published via Towards AI