A strong retrieval system is only as good as the data it’s built on. Before focusing on models or infrastructure, it’s important to ensure your underlying data is well-structured, consistent, and enriched with useful context. This includes extracting and standardizing metadata (e.g., document source, date, type, author), as well as organizing data into a format that can be reliably indexed and retrieved.

Beyond basic organization, teams should think carefully about document processing decisions such as chunking and categorization. For example, breaking documents into meaningful semantic units (rather than arbitrary lengths) can significantly improve retrieval quality. It’s also helpful to track versioning and lineage early on, so you can understand where data came from and how it has changed over time. These decisions are much harder to retrofit later, so investing in them upfront will save time and improve system performance.

Additional Resources:

• How to build accurate RAG over structured and semi-structured databases (Medium)
• Building an unstructured data pipeline for RAG (databricks)
• Retrieval Augmented Generation (pinecone.io)
• Text splitter integrations (langchain)
• Designing Data-Intensive Applications (book, paywall)

This response has been generated by an LLM based on notes from PJMF technical consultations. All responses go through human review by our PJMF Products & Services team and are anonymized to protect our consultation participants.