Retrieval Augmented Generation (RAG)
Description
Large language models like GPT-4 are impressive, but they have two significant flaws: they are frozen in time (limited to the data they were trained on) and they are prone to "hallucinations" (inventing facts confidently). For global enterprises, these flaws are unacceptable. You cannot have a customer support bot inventing warranty terms or a technical assistant citing safety protocols that changed last week.
RAG solves this by adding a crucial step to the AI process. When a user asks a question, the system does not immediately generate an answer from memory. Instead, it first searches a trusted repository – such as a company’s knowledge base, Component Content Management System (CCMS) or technical documentation – to find relevant facts. It then feeds this retrieved information into the LLM along with the original question. The LLM acts as a synthesizer, using the retrieved facts to construct a natural, accurate response. For RAG to work effectively, the "textbook" must be readable. This is why structured content is critical. When content is broken down into semantic components and tagged with metadata, RAG systems can retrieve precise answers rather than vague documents, ensuring the AI output is grounded in truth.
Example use cases
- Chatbots: Delivering answers based on live product manuals and FAQs rather than outdated training data.
- Assistants: Allowing technicians to query complex repair databases using natural language.
- Legal: Drafting responses or summaries that cite specific clauses from an internal database.
- Knowledge: Enabling employees to search across intranets and technical files to find accurate company policies.
- Marketing: Generating content that combines an LLM’s creativity with specific product inventory data.
Key benefits
RWS perspective
At RWS, we believe that Retrieval Augmented Generation is the bridge between generative potential and business reality. However, we know that RAG is only as good as the data it retrieves.
We help organizations build the "Golden Source" for their RAG systems through Tridion. By managing content as structured, semantic components, we ensure that retrieval systems can pinpoint the exact information needed. Furthermore, our Semantic AI capabilities enrich this content with metadata, helping the machine understand the intent behind a query, not just the keywords. This Human + Technology approach ensures that your AI agents speak with the authority of your best experts, delivering answers that are safe, compliant and accurate.