Everyone Says “Just Use RAG.” Here’s Why That’s Not Enough

Retrieval-Augmented Generation is often treated as the default answer whenever an AI system needs access to business data.

But adding a vector database does not automatically create a reliable system.

In Episode 8 of System Prompt, we examine what actually goes into useful RAG pipelines: prompting, keyword search, data quality, canonicalization, storage, retrieval, cost, testing, and human review.

We also discuss where fine-tuning fits, why it solves a different problem from RAG, and why AI systems should be developed iteratively rather than treated as one-time implementations.

WHAT WE DISCUSS

• Why prompting still affects model performance

• The difference between keyword and semantic search

• What RAG actually does

• Why RAG does not guarantee accurate answers

• How data quality affects retrieval quality

• Canonicalization and normalization

• Reduc