RAG: Retrieval to Reality

RAG is a way to make AI tools smarter by letting them look up information before they answer you. This makes answers more accurate, more current, and more grounded in real data.

AI tools like ChatGPT and Copilot are powerful, but they don’t know your business. They can’t access your policies, your company’s information, or data that matters to your organisation. And when these AI tools don’t know something, they don’t always admit it.

That’s where Retrieval-Augmented Generation (RAG) comes in.

RAG is a way to make AI tools smarter by letting them look up information before they answer you. This makes answers more accurate, more current, and more grounded in real data.

But here’s the catch: building RAG properly is harder than it sounds. In practice, even experienced teams run into the same problems. This could be gaps in security, unreliable answers, and an AI tool that finds information without really understanding it.

This insight breaks down Retrieval-Augmented Generation (RAG) and shows how COMPASS™ fixes the issues that traditional RAG leaves behind.

What is Retrieval-Augmented Generation (RAG)?

To put it simply, RAG makes AI tools smarter by letting them look up information before they answer you. Instead of relying solely on pre-trained data, RAG retrieves relevant facts first and builds more current, accurate answers around them.

The Positives of Using RAG

Because RAG-based AI does not rely solely on its training data, it offers several benefits for the general user experience:

• More accurate answers: By pulling in relevant information before responding- rather than relying only on training data- the AI produces more precise results.

• Can reduce hallucinations: AI models can make up things when they don’t have an answer to something. RAG reduces this issue by grounding its responses in retrieved information- though it has limitations we’ll discuss later.

• Extends the AI’s knowledge: RAG removes the limits of what the AI learned during training. RAG effectively gives it an unlimited, updatable memory.

The Limitations of Using RAG

Most RAG projects start off looking really promising. You ask the AI a few questions, it retrieves relevant information, and everything appears to work because RAG is a solid, established approach. But in practice, many RAG projects stall, as building an effective pipeline demands deep expertise- chunking strategies, prompt logic, hallucination controls, and more.

Most teams don’t have access to the specialised knowledge needed to build RAG successfully, which can result in several flaws:

• Security concerns: Without proper access controls, users may retrieve content they shouldn’t , creating a risk of exposing sensitive company data.

• Hallucinations: Instead of admitting they don’t know an answer, AI fills the gap by generating something plausible that isn’t true. This creates confident but incorrect answers that can damage users trust.

• Chunking vs. Answering: When a RAG system analyses documents, it breaks them into smaller, searchable pieces- a process called ‘chunking’. The problem is that finding the most relevant chunk isn’t the same as having enough context to answer the question correctly. To put it simply, finding the right information is not the same as correctly answering the question. This can lead to incomplete or misleading responses – something businesses should be cautious of in high-stakes environments.

Why RAG alone isn’t enough.

RAG is a powerful concept- but implementing it effectively is where most organisations struggle.

Fidra designed the COMPASS™ platform specifically to address these challenges. COMPASS™ is a ready-made platform enabling organisations to quickly and securely deploy an AI chatbot over their internal data.

COMPASS™ is a standard RAG system that retrieves relevant internal data before generating answers. A strengthened RAG pipeline with tuned retrieval, context-preserving chunking, and built-in security so responses are more accurate, reliable, and controlled.

Below are the ways in which platform-based approaches like COMPASS™ address the limitations brought about by traditional RAG AI systems:

• Reduced hallucination risk: COMPASS™ uses tuned retrieval and prompting logic specifically designed to minimise hallucinations. This allows users to get accurate answers they can trust- without needing to understand how RAG works behind the scenes.

• Increased Security: COMPASS™ connects and retrieves information from existing data sources and respects who was already allowed to access certain information. If an individual couldn’t access underlying data before, the COMPASS™ chatbot wouldn’t show it to them either. Additionally, your company data doesn’t leave the building, as COMPASS™ can run entirely on your own servers- meaning sensitive data is less likely to be accessed by third-party platforms.

• Smarter answers, not just closer matches: COMPASS™ delivers smarter answers through a pre-built, tuned retrieval and prompting layer -meaning the way that documents are chunked and retrieved has been engineered specifically to preserve context, rather than finding just the closest matching/ most relevant text.

Visual Explanations of the Technicalities:

Below are two diagrams that help us visually compare the more traditional RAG pipeline to how COMPASS™ operates.

From the diagram above, we can see that a traditional RAG pipeline begins with an ‘input’ – the user asking a question. Then it retrieves relevant information to the question asked by searching within its knowledge sources, identifying the most relevant ‘chunks’ of information to the inputted prompt (1). After this is the augmentation part of RAG, where the system builds an enriched prompt that contains the user’s original question and the retrieved documents or excerpts (2). Finally, the LLM generates a response using a combination of the retrieved information and its own knowledge (3).

Above, we can see that the process is slightly different. In COMPASS™, the LLM doesn’t just used retrieved contextual information, but it actively drives the retrieval process.

COMPASS™ begins in the same way, with the user asking a question. The LLM then interprets what this question means, what information it needs, and where this information might live. Then, COMPASS™ searches through multiple sources, returning only the most relevant context back to the LLM. This part ensures that the answers given are organisation-specific, compliant, and up to date.

The LLM actively evaluates the retrieved information- the key factor that distinguishes the two RAG models shown in the diagrams. The LLM will ask itself whether it has enough information to give an accurate answer, or whether the context is complete, and if the answer is NO the LLM triggers another search to refine its answer- creating an iterative retrieval loop. When the LLM judges that it has sufficient, relevant, high-quality context, it produces a final answer that is contextualised, grounded in retrieved organisational knowledge, and tailored to the user’s needs.

The final product is an agentic RAG model, one that thinks, searches, and iterates until the answer is genuinely good.

The Bottom Line:

Traditional RAG systems represent a meaningful step forward in making AI more accurate and contextually aware. But as we’ve explored, challenges around hallucinations, security, and the gap between retrieving information and accurately answering questions are real barriers that organisations face when trying to use AI to assist their productivity.

Platform-based approaches like Fidra’s COMPASS™ are built precisely to bridge that gap,  offering organisations a secure, ready-to-deploy solution that handles the technical heavy lifting, so teams can focus on what the data can do, not on how to make it work.

RAG makes AI smarter- but COMPASS™ makes it reliable, secure, and useable in a business.

If you’d like to learn more about how COMPASS™ could work for your organisation, please get in touch by emailing us at info@fidra.ai or visit our dedicated COMPASS™ product page.

Back to Insights