Hierarchical RAG: Building Multi‑Level Retrieval for Scalable Knowledge Systems

Introduction

A language model is only as good as what it can retrieve. As knowledge bases grow into millions of documents, standard Retrieval-Augmented Generation starts to crack, not because the models are weak, but because the retrieval layer is too shallow. A single-level vector search often pulls back chunks that are semantically similar but contextually wrong, which leads to hallucinations, missed facts, or slow inference.

Hierarchical RAG, drawing on the "Efficient and Faithful Retrieval-Augmented Generation via Hierarchical Retrieval" paper, fixes this by stacking retrieval layers instead of searching the whole knowledge base at once. It narrows the search progressively: first the right document, then the right section, then the right chunk. The result is more accurate, more faithful, and scales much better.

At Hoomanely, where we index millions of dog-health pages, veterinary books, research papers, and internal knowledge, this design has become fundamental. Getting it right shapes how reliable our health insights are, how good our QnA responses are, and how safe our explanations feel to pet parents.

Why flat RAG fails at scale

Even solid RAG implementations run into three problems once the corpus gets large: low precision, where vector search returns chunks that are semantically similar but contextually off; low faithfulness, where the LLM hallucinates because the retrieved context is incomplete or misleading; and high latency, since searching millions of chunks gets expensive fast. A flat pipeline treats every chunk the same and searches all of them at once. Past a million entries or so, cosine similarity and ANN indexes start to degrade, and the model ends up swimming in noise.

This shows up in enterprise search over policies, legal docs, and manuals; in scientific literature like Hoomanely's medical corpus; and in multi-modal systems combining text, images, and metadata. Hierarchical retrieval tackles this by finding the right container first, then navigating inside it.

Multi-level hierarchical retrieval

The approach creates three layers of retrieval. Document-level retrieval, the coarse pass, represents an entire document or long PDF with a single embedding and picks the top-k most relevant documents, which shrinks the corpus from millions of chunks down to maybe fifty or a hundred documents. Section-level retrieval, the medium pass, embeds each chapter, section, or heading within the selected documents and picks the top sections, dropping the search space to a few hundred candidates. Chunk-level retrieval, the fine pass, only searches chunks inside the shortlisted sections.

All documents
→ Selected documents
→ Selected sections
→ Selected chunks

This works because semantic distance drops sharply at each level, noise falls away as irrelevant sections get filtered out, and latency drops because each step shrinks the search space. The paper behind this approach reports 14 to 19 percent improvements in answer grounding, since the LLM sees context that's actually part of the right section, and we've seen similar gains in our own pipelines at Hoomanely.

Building hierarchical RAG in practice

Step 1, multi-level chunking: split the text into three granularities, a document summary of one to three paragraphs, section summaries a few hundred words each, and atomic chunks of 300 to 500 tokens. Each level gets its own embedding.

Step 2, build parallel vector indices: maintain three separate vector stores, one for documents, one for sections, and one for chunks.

Step 3, query routing: embed the user's query, search the document index for the top-k, search only the sections within those documents for their own top-k, then search only the chunks within those sections.

Step 4, fusion and reranking: use an LLM or embedding-based reranker to make sure the final chunks are relevant, non-redundant, and faithful.

Step 5, generation: feed only three to six tightly curated chunks to the LLM, which then grounds its answer in context that's actually meaningful instead of a wide, noisy pull.

What hierarchical RAG improves

Retrieval faithfulness: answers cite sections that actually contain the ground truth, with a 14 to 19 percent accuracy gain on grounded QA tasks in the paper's findings.

Latency: shrinking the search space cuts ANN workload, giving 20 to 40 percent faster retrieval on large vector stores.

Hallucination: the LLM works from internally consistent context, cutting hallucination by up to 35 percent depending on corpus quality.

Multi-task adaptability: the hierarchical structure supports summaries, explanations, retrieval chains, and evidence extraction, which matters for Hoomanely's medical QnA engine, where a query might range from "Is panting normal?" to a detailed question about corneal ulcer symptoms drawn from veterinary literature.

Where hierarchical RAG shines

Large PDF repositories, from legal contracts to manuals and textbooks, anything organized into chapters. Scientific literature retrieval, much like what we do for dog physiology and health. Enterprise knowledge bases, where content ranges from Slack conversations up through document summaries and SOPs down to detailed chunks. And multimodal systems, where sections can include captions, metadata, and OCR chunks.

Takeaways

Hierarchical RAG is the next real step forward for retrieval systems, and it fixes the bottleneck that actually exists (retrieval precision) rather than pushing for bigger LLMs. A single vector search isn't enough for large corpora. Multi-level retrieval cuts noise substantially. Faithfulness improves when retrieval follows document structure. Latency drops because the search space shrinks early. And it works especially well for large PDFs and multi-chapter documents. At Hoomanely, this method strengthens how we generate medical insights, answer pet-parent questions, and keep our AI explanations grounded in verified veterinary knowledge.