At the heart of any RAG system is the retrieval pipeline, and chunking is its starting point. Chunking splits documents into smaller passages that can be indexed and retrieved effectively without losing too much meaning. Once chunks exist, embedding search converts the text into vectors and finds semantically similar passages even when exact keywords differ, allowing the system to match questions to relevant content based on meaning rather than literal wording.
Reranking then reorders retrieved passages using a stronger relevance model so the most useful context appears first in the prompt. This two-stage approach, broad retrieval followed by focused reranking, often outperforms using either stage alone. Retrieval quality is what ties these pieces together, measuring whether the right documents are found for a user question, and it matters because poor retrieval leads to poor answers even when the model is strong. A common mistake is blaming the model when the retrieved context was irrelevant.
When retrieval succeeds, source citation links the generated claims to the supporting documents or records, letting users verify answers and increasing trust. A common mistake here is citing sources that were not actually used or do not support the claim, which undermines the very trust citation is meant to build. Practicing retrieval quality and citation in a small realistic example, and asking what problem each technique is solving, what trade-off it creates, and how the result will be measured, makes these habits reliable before they are deployed at scale.