Inside a RAG System: Architecture and Best Practices

Understanding what RAG can do is one thing. Understanding how it actually works is another. For technical teams tasked with implementing AI solutions, and business leaders evaluating vendor proposals, knowing the architecture beneath the surface makes the difference between a system that transforms operations and one that disappoints.

The reality is that RAG systems vary dramatically in sophistication and effectiveness. A basic implementation might retrieve random document chunks and feed them to a language model, producing inconsistent results. An advanced system coordinates multiple specialized components, each tailored for a specific function, to provide reliable answers based on verified information. The difference lies entirely in the architecture.

At its core, RAG operates in two main stages. First, the retrieval component searches across a company’s knowledge base to identify the most relevant documents or data points. Next, the generation component uses a language model to synthesize this information into a coherent response. This reduces hallucinations, improves accuracy, and ensures that answers are grounded in verified content. When connected to an internal LLM, this process also keeps proprietary information private and secure.

 The Building Blocks of RAG Technology

The first building block is the document store, where all the company’s knowledge lives — from product manuals and research papers to policies and FAQs. A well-organized knowledge base is essential because the quality of retrieval depends entirely on the quality of stored information. Some businesses rely on simple databases, while others adopt sophisticated vector databases designed for semantic search, capable of handling millions of documents efficiently.

After the knowledge base is organized, the next step is making it searchable. This is where indexing and embeddings come in. Indexing breaks documents into smaller, searchable chunks, while embeddings convert each chunk into a numerical representation that captures its meaning. This allows the system to find relevant information even when the user’s query doesn’t use the exact same words, making retrieval much smarter.

Once the data is indexed, the retrieval engine takes over. It compares the user’s query with stored embeddings and surfaces the most relevant context in milliseconds. This stage is crucial because it determines what information the language model will use to craft a response. A well-tuned retrieval engine dramatically improves accuracy and reduces irrelevant results.

The process then moves to the generation stage, where the language model takes the retrieved context and weaves it into a coherent, human-like answer. Fine-tuning or prompt engineering ensures the output matches the company’s tone and uses domain-specific terminology, making the response feel trustworthy and professional.

Finally, some companies add a verification layer before presenting the answer to the user. This might involve rule-based checks, re-ranking, or even another model that validates the facts. While not strictly required, this step helps reduce the risk of passing along outdated or incorrect information.

 Best Practices for Building RAG Systems

Creating a robust RAG solution involves more than connecting a search engine to a model. Companies should:

• Curate their knowledge base to remove outdated or irrelevant information.

• Continuously monitor performance with feedback loops and logs.

• Retrain embeddings as new information becomes available.

• Implement security controls including access management and encryption.

• Communicate transparently with users about how responses are generated.

Since RAG systems often access sensitive data, strong access control and compliance measures are essential. Encryption and role-based permissions help ensure that internal knowledge remains private. 

How Lineate Delivers Effective RAG Solutions

Lineate has implemented RAG systems for a range of companies and, over the years, has honed the expertise that delivers effective solutions. One of the most recent projects involved integrating Yooz Invoice into DataLake, loading historical P&L data, and building dashboards to support the 2026 budgeting process.

In addition to financial integration, Lineate automated the summarization and recommendation of Guest Reviews for ten brands, presenting the results through an interactive Quicksight dashboard. The team also developed proofs of concept demonstrating how unstructured Guest Reviews data could be made accessible to users via AI chat, leveraging the new AWS service, Amazon Q for Business.

While the initial deliverables provided immediate operational value, the project also highlighted the transformative potential of applying AI to the company's private enterprise data. This ensures that insights remain secure while enabling faster, smarter decision-making.

Lineate’s RAG solutions showcase how AI-powered systems can transform operations, streamline workflows, and unlock actionable insights from complex data—all within a secure environment.

If your organization plans to harness the full potential of its data and drive smarter, faster decision-making, explore how Lineate’s expertise in AI and RAG systems can help you achieve measurable impact.