How to Build and Test RAG Systems for Accuracy

UWUCC Workforce Learning has released a set of practical resources and training guidance focused on building generative AI applications, particularly retrieval-augmented generation (RAG) systems that answer questions from an organisation's own documents. The materials include guides on how such AI applications find answers in users' documents, how to build and test them for accuracy, and a glossary that defines common AI terms in plain English.

The site also explains how embeddings and vector databases help AI tools search by meaning, compares prompting, retrieval-augmented generation and fine-tuning, and details how RAG enables an AI assistant to answer from a user's own documents. Visitors are invited to ask about the site's course, suitable dates, entry requirements and funding, and a link is provided to a GenAI Builder Sprint course.

Understanding Retrieval-Augmented Generation

Retrieval-augmented generation is a technique that combines a large language model with a retrieval system. In a typical setup, a user asks a question, the system searches a database of documents to find relevant passages, and then the language model uses those passages to compose an answer. This approach allows the AI to ground its responses in specific, verifiable sources, reducing the risk of making up information or relying on outdated training data.

For organisations that want to deploy an AI assistant that answers questions about internal policies, product documentation or research reports, RAG offers a way to keep the assistant's knowledge current without retraining the entire model. The retrieved documents act as a live knowledge base that can be updated as needed.

Why Accuracy Testing Matters

Building a RAG system is only half the work. Ensuring that the system returns correct, relevant and complete answers requires careful testing. A RAG evaluation typically checks whether the retriever finds useful documents and whether the generator uses those documents correctly to form the answer.

One common approach is to create a set of test questions with known answers drawn from the document collection. The system's responses are then scored on metrics such as answer correctness, relevance and faithfulness to the source documents. Faithfulness means the answer does not contradict the retrieved passages, even if it states something that is true in general but absent from the sources.

Without a proper evaluation routine, organisations risk deploying a system that sounds plausible but gives incorrect answers. This is especially important when the AI assistant handles questions with real-world consequences, such as compliance inquiries or safety procedures.

How Embeddings and Vector Databases Enable Search by Meaning

At the core of most modern RAG systems is the use of embeddings and vector databases. An embedding is a numerical representation of a piece of text that captures its meaning, not just its exact words. When a query comes in, the system converts it into an embedding and searches a vector database for documents whose embeddings are most similar. This allows the system to find relevant information even when the user's phrasing differs from the original document.

For example, a user asking "What is the refund policy?" might retrieve a document titled "Return of Goods Procedure" because the embeddings of both texts are close in meaning. This capability makes RAG systems far more flexible than traditional keyword search.

However, the quality of the embeddings and the design of the retrieval strategy directly affect the accuracy of the overall system. A poorly tuned retriever may return irrelevant documents, leading the language model to produce an incorrect or confusing answer.

Comparing Prompting, RAG and Fine-Tuning

Organisations building generative AI applications often weigh three main approaches: prompting, retrieval-augmented generation and fine-tuning. Prompting involves giving the language model a carefully crafted instruction and relying on its pre-trained knowledge. Fine-tuning means further training the model on a specific dataset to adapt its behavior. RAG sits between them, offering a way to inject fresh information without retraining.

Each approach has trade-offs. Prompting is quick but limited by the model's existing knowledge. Fine-tuning can produce deep customisation but requires significant data and compute resources. RAG offers flexibility and freshness but introduces a new dependency on the retrieval system's accuracy. Many teams use a combination, for instance fine-tuning the model on the domain's language while using RAG to supply current facts.

Practical Steps for RAG Evaluation

Conducting a thorough RAG evaluation involves several steps. One approach is to define a set of test queries that cover typical use cases. For each query, the expected answer is known. The system's answer is then scored on correctness, completeness and faithfulness to the retrieved documents.

Another useful check is to examine the retrieval step in isolation. Does the retriever return at least one relevant document for each query? If not, the answer may be based on little or no supporting evidence. Teams can also inspect the system's output for hallucinations, where the model states something not found in the retrieved passages.

Some teams also measure the system's performance on edge cases, such as ambiguous questions or queries that require reasoning across multiple documents. This helps uncover weaknesses in both the retriever and the generator.

Developing a Testing Culture

For organisations that are new to generative AI, building a testing culture around RAG systems is an important step. The UWUCC Workforce Learning resources highlight how to build and test RAG applications for accuracy. The site's glossary and comparisons provide a foundation for teams to understand the concepts before they start building.

Regular testing helps catch regressions when the document collection changes or the language model is updated. It also provides a baseline for deciding whether a change improves or degrades the system. Teams that invest in evaluation early tend to have more reliable systems in production.

The site also invites visitors to ask about its course, suitable dates, entry requirements and funding. The GenAI Builder Sprint course linked from the page offers a structured program for teams that want hands-on experience building and testing generative AI applications.

As more organisations adopt RAG for internal knowledge assistants, customer support bots and research tools, the ability to evaluate these systems thoroughly will become a core skill. A solid RAG evaluation process not only improves accuracy but also builds trust in the AI assistant's answers.