RAG Architecture Guide for Large-Scale Document Search

The 600,000-Page Challenge

Every organization has a treasure trove of knowledge locked away in documents. Recently, I was presented with a common but significant enterprise challenge: “We have 20,000 documents, each about 30 pages long. We need to index them and enable our team to ask questions and get answers from them.”

This isn’t a small task. A quick calculation reveals the scale of the data: we are looking at a 600,000-page corpus of internal knowledge. The goal is not just to store this information, but to transform it into a dynamic, conversational AI system that can provide employees with accurate, verifiable answers in real-time.

Why the Naive “Chunk and Embed” Approach Fails at Scale

When faced with this problem, the first solution that often comes to mind in the age of LLMs is a simple Retrieval-Augmented Generation (RAG) approach: split every document into fixed-size chunks and create a vector embedding for each piece. While this can work for small-scale demos, it fails spectacularly when confronted with a true enterprise-level challenge. This naive strategy quickly runs into three critical roadblocks:

1. Prohibitive Cost: The financial outlay for embedding and storing millions of text chunks, and performing vector searches across them, is immense.
2. Low Precision: Fixed-size chunks have no respect for the original document’s structure. They often split a table from its title or a sentence from its conclusion, feeding the LLM a noisy, disjointed context that leads to poor or incorrect answers.
3. Poor Recall: A simple vector search can easily miss relevant documents if a user’s query uses different terminology or keywords than what’s in the text, creating a “semantic gap” that the system can’t bridge.

To overcome these hurdles and build a system that is not only powerful but also sustainable, a more sophisticated architecture is required. We need a multi-stage blueprint designed to efficiently balance cost, speed, and accuracy for massive document sets.

A Two-Level Indexing Strategy

Before we can even think about retrieval, we must first process our 600,000 pages of documents into a format that is both intelligent and optimized for search. This indexing stage is the most critical part of the entire system. Getting it wrong guarantees poor performance down the line.

The first and most important step is to abandon fixed-size chunking. This context-blind approach is a primary source of poor RAG performance because it butchers a document’s logical flow by splitting paragraphs mid-thought or separating a table from its caption. Instead, we must employ Layout-Aware Hierarchical Chunking. This technique intelligently parses each document according to its inherent semantic structure. Rather than arbitrarily counting characters or tokens, it identifies the natural divisions that give the document its meaning, such as titles, sections, subsections, tables, and figures. Each of these logical units becomes a “chunk,” ensuring the information within remains contextually complete.

While intelligent chunking gives us high-quality, context-rich chunks, searching over the full text of these chunks is still inefficient at scale. The real key to balancing performance and cost lies in creating a two-level index. This strategy involves generating two separate but linked representations for each semantic chunk.

The first level is the Search Target, which forms our lean and efficient index layer. For each chunk, we create a concise, AI-generated summary of about 100 to 200 tokens, including the section’s original title. This small, dense representation is optimized for fast and efficient searching. The second level is the Payload, which is the complete, original text of the semantic chunk, typically ranging from 400 to 900 tokens. This is the rich, detailed content that we will eventually provide to the LLM to formulate its answer, acting as our “document store.”

This separation is super important to building a cost-effective system at scale, and to be able to answer questions with high precision and recall. We perform our initial, broad searches only on the tiny, inexpensive abstracts. This way, we only incur the cost of retrieving and processing the larger, full-text payload for the small handful of chunks that we have already identified as highly relevant. The process is analogous to using a library catalog to find the right book on the shelf, rather than wastefully reading every single book just to find the information you need.

The Gold Standard 3 Stage Retrieval Funnel

With our documents intelligently indexed, the next challenge is to find the right information at query time. We need a retrieval funnel that can efficiently sift through hundreds of thousands of abstracts to find the few that are most relevant to a user’s question. This is achieved not with a single search, but with a cascading, three stage funnel where each step progressively refines the results of the previous one.

The process begins with a Sparse Search, which acts as the first broad filter. Using a fast, keyword-based algorithm like BM25, we perform a search across the entire index of document titles and abstracts. The goal here is cheap, wide recall. This initial step quickly narrows the vast search space from hundreds of thousands of potential documents down to a more manageable list of a few hundred candidates that show strong keyword relevance. This stage is designed to be fast and inexpensive.

Next, we take the top results from this initial sparse search and pass them to the second stage: Dense Vector Search. Instead of searching the entire corpus, this more computationally expensive semantic search is only performed on the pre-filtered list of candidates. This approach dramatically reduces the workload. The dense search then reranks this smaller candidate list based on conceptual meaning, identifying abstracts that are semantically similar to the user’s query, even if they do not share the exact same keywords.

Finally, the top results from the dense vector search are passed to the last and most precise filter, Cross-Encoder Reranking. This is the most analytically powerful stage. A cross-encoder model takes the user’s query and directly compares it against each of the top candidate abstracts from the dense search stage, one by one. This deep analysis provides the most accurate relevance score possible. The purpose of this final step is to take the semantically relevant list and score it with surgical precision, allowing us to confidently identify the absolute top 10 to 20 “sharpest” abstracts. This ensures the context we pass to the final stage is of the highest possible quality and relevance.

Synthesis and Verification

The system now holds a small, highly relevant list of the top 10 to 20 abstracts. This is the raw material for our answer, but it is not yet the final product. The last steps involve retrieving the full context associated with these abstracts and performing a series of crucial post-retrieval optimizations before synthesis.

The first action is to retrieve the corresponding full text payload for each winning abstract. This is where our two-level indexing strategy pays its dividends. The system uses the identifiers from the top-ranked abstracts to fetch the complete, original text chunks from our document store. Instead of a short summary, we now have the rich, detailed content that contains the actual information needed to answer the user’s question thoroughly.

Before this context is sent to the LLM, it undergoes several critical post-retrieval optimizations. First, to provide richer surrounding context, the system will pack adjacent hits. This means that for a highly relevant chunk, its preceding and succeeding chunks might also be retrieved to ensure no crucial information is lost at the boundaries. Second, the system will deduplicate aggressively. It is common for a retrieval process to return multiple chunks containing overlapping or identical information. These redundancies are identified and removed to create a more concise context and make the best use of the LLM’s attention.

Finally, and most importantly, each piece of information is augmented with a verifiable section path citation. For example, a citation like (Document_Name.pdf > Section 4.2 > Table 3) is appended to its corresponding text. This step is what transforms the system from a black box into a trustworthy assistant, as it gives the user a direct path to verify the source of any claim. This process is sometimes referred to as ‘citing the section path’.

Finally, the system generates the answer. The optimized context and the original user query are sent to the Large Language Model. The LLM receives a critical instruction: it must construct its answer based only on the text provided, ignoring its own general knowledge. The model’s task is to read the information and then write a clear answer that integrates the source citations. This process ensures the output is not a generic response from the model’s training data. Instead, it is a specific answer derived directly from the source documents, with a clear path for the user to verify where each piece of information came from.

TLDR; The 3 Stage Retrieval Funnel

To bring all these stages together, here is a complete blueprint of the architecture from document ingestion to final answer. This table serves as a quick reference for the entire workflow, demonstrating how each step systematically refines the quality of the information.

Stage	Action	Technology	Target	Goal
1. Indexing	Hierarchical Chunking	Layout-Aware Parsing	Source Documents	Create a two-level index: short abstracts & full-text payloads.
2. Retrieval	Sparse Search Filter	BM25	All Titles & Abstracts	Fast, wide recall; create an initial candidate list.
3. Retrieval	Dense Search Refinement	Vector Database	Top candidates from Stage 1	Semantic reranking of the candidate list.
4. Retrieval	Precision Reranking	Cross-Encoder	Top candidates from Stage 2	Isolate the top 10-20 most relevant chunks for the LLM.
5. Synthesis	Answer Generation	Large Language Model	Optimized, full-text chunks	Generate an accurate, verifiable, and cited answer.