RAG Architecture Patterns for Enterprise Knowledge Systems

Beyond Basic RAG

Retrieval-Augmented Generation has become the default architecture for enterprise AI applications that need to work with proprietary data. The basic pattern is straightforward: retrieve relevant documents from a knowledge base, inject them into a prompt, and let the language model generate a grounded response.

But basic RAG falls apart at enterprise scale. Documents number in the millions. Knowledge spans dozens of systems. Users expect accurate, sourced answers with sub-second latency. Getting RAG right for the enterprise requires deliberate architectural choices.

The Chunking Problem

How you break documents into retrievable pieces is the single most impactful decision in a RAG pipeline. Get it wrong, and your system will retrieve irrelevant context — no matter how good your embedding model is.

Fixed-size chunking (splitting every N tokens) is simple but crude. It breaks semantic boundaries, splitting paragraphs mid-thought and separating headers from their content.

Semantic chunking uses natural language processing to identify meaningful boundaries — paragraphs, sections, topic shifts. This preserves context but requires more processing and can produce inconsistent chunk sizes.

Hierarchical chunking maintains parent-child relationships between document sections. A query might match a specific paragraph, but the system retrieves the surrounding section for context. This dramatically improves answer quality for complex documents.

For enterprise deployments, we recommend hierarchical chunking with metadata preservation. Every chunk should carry its source document, section headers, creation date, and access permissions.

Embedding Strategy

The choice of embedding model determines how well your system understands the semantic relationship between queries and documents. Key considerations:

Domain-specific vs. general-purpose: General models like OpenAI embeddings work well for broad content. But for specialized domains (legal, medical, financial), fine-tuned embeddings significantly outperform generic alternatives.

Dimensionality trade-offs: Higher-dimensional embeddings capture more nuance but increase storage costs and retrieval latency. For most enterprise use cases, 768 to 1536 dimensions provides the right balance.

Hybrid search: Combining vector similarity with traditional keyword search (BM25) consistently outperforms either approach alone. Vector search captures semantic meaning. Keyword search catches exact matches that embeddings might miss.

Retrieval Architecture

At enterprise scale, naive nearest-neighbor search is insufficient. Production RAG systems need:

Multi-stage retrieval: First, cast a wide net with fast approximate search (retrieving 50-100 candidates). Then, re-rank using a cross-encoder model that evaluates query-document pairs more precisely. Finally, select the top 5-10 chunks for context injection.

Query transformation: Users rarely write perfect search queries. Before retrieval, transform the user query into multiple reformulations — hypothetical document snippets, keyword extractions, and decomposed sub-questions. Retrieve against all transformations and merge results.

Contextual compression: Retrieved chunks often contain irrelevant information alongside the relevant passage. A compression step that extracts only the pertinent sentences reduces noise and allows you to fit more relevant context into the model window.

Handling Multi-System Knowledge

Enterprise knowledge lives across dozens of platforms — SharePoint, Confluence, Salesforce, internal wikis, email archives, Slack channels, and structured databases.

A production RAG system needs:

• Unified ingestion pipeline: Connectors for each source system with standardized output format
• Access control propagation: If a user cannot access a document in the source system, they should not receive answers from it in the RAG system
• Incremental updates: Re-indexing millions of documents on every change is impractical. Use change detection and incremental embedding updates
• Source attribution: Every answer must link back to its source documents so users can verify and explore further

Evaluation and Monitoring

RAG systems degrade silently. Documents become outdated. Embedding drift occurs as language patterns shift. New content types break chunking assumptions.

Production systems need continuous evaluation:

• Retrieval relevance scoring: Are the right documents being retrieved for test queries?
• Answer faithfulness: Is the model grounding its responses in retrieved content, or hallucinating beyond the source material?
• Latency monitoring: Is retrieval performance staying within acceptable bounds as the knowledge base grows?
• User feedback loops: Thumbs up/down ratings and correction tracking to identify systematic failures

Getting Started

Do not try to index everything at once. Start with one high-value knowledge domain, get the pipeline right, prove accuracy, and expand. The organizations that succeed with enterprise RAG are the ones that treat it as an engineering discipline, not a weekend hackathon.