Chunking, Hybrid Retrieval, and Reranking
Explore how chunking strategies, sparse and dense retrieval methods, and rerankers work together to enhance retrieval quality in generative AI systems. Understand trade-offs in chunk size and overlap, recognize failure modes, and learn to design retrieval pipelines that optimize recall, precision, latency, and prompt budget for effective RAG system design.
A support assistant can run on a strong LLM and still fail a simple question such as “Which firmware version introduced this error code?” when the retrieval layer surfaces a marketing summary instead of the release note and troubleshooting step that contain the answer.
That is the normal RAG failure mode. The generator is capable, but the evidence path is weak. In system design interviews, the bottleneck is usually not token generation. It is candidate recall, ranking quality, latency, and how much prompt budget you spend on weak context.
Why retrieval choices dominate RAG quality
A retrieval pipeline behaves like the control plane for grounded generation. The request enters the system as a user query, passes through chunk indexes and ranking stages, and only then reaches the model. If the right evidence is absent or buried, the model either answers vaguely or fills gaps with plausible text.
A useful way to defend design choices is to track four budgets throughout the request life cycle.
Recall: The system must place the right evidence somewhere in the candidate set.
Precision: The top results must contain mostly relevant text rather than semantically nearby noise.
Latency: Each extra branch, expansion, and ranking pass adds work before generation starts.
Prompt budget: Retrieved text consumes context window space that could otherwise hold instructions, memory, or output tokens.
Note: In production, retrieval tuning often produces larger quality gains than switching to a larger LLM.
Retrieval design improves answer grounding by controlling how documents are split, how candidates are generated, and ...