In today’s business environment, data is among the most valuable assets. The ability to find, process and use the right information is essential for maintaining competitiveness. Large Language Models (LLMs) are becoming indispensable tools for this purpose but they have their limitations. Traditional LLMs are trained on vast datasets which may not reflect the specific requirements of a given business. For those operating in the specialist sector, internal documents and knowledge repositories are often far more relevant than general information. This is where Retrieval Augmented Generation (RAG) becomes invaluable.
What is retrieval augmented generation and when is it needed?
RAG enhances LLMs by combining them with information retrieval. This allows the model to access external knowledge sources such as documents, databases and other data stores to provide more accurate and contextually relevant answers.
Historically, fine-tuning was a popular alternative. This involved retraining the model using a specialised dataset. While fine-tuning can be effective it integrates information permanently into the model. Any changes to data require the model to be retrained which can be time-consuming and costly.
By contrast, RAG retrieves fresh information in real time. This makes it ideal for dynamic knowledge bases, evolving product information, market trends and more. How does RAG improve the accuracy of AI responses? For example, a customer support chatbot using RAG can always access the latest product data. A fine-tuned model would quickly become outdated unless continually retrained.
Vector databases: understanding meaning, not just keywords
At the core of RAG is a vector database. It stores numerical representations or vectors of documents, articles and other content. Text is divided into chunks and encoded into these vectors using embedding models. When a user submits a query, the system retrieves semantically similar chunks rather than relying on keyword matches.
For instance, a question such as “How do I set up a secure connection?” might return related information even if the exact wording does not appear in the query. This makes RAG valuable across a wide range of business content.
Embedding models transform text into vectors. A widely used retrieval augmented generation example is OpenAI’s text-embedding-ada-002, often paired with GPT-4 in RAG solutions. Selecting the right embedding model is critical as better vectors lead to more relevant results.
To improve relevance, many RAG systems now include a reranking stage. After retrieving candidate documents or chunks a reranker model evaluates which pieces are most likely to answer the query. This ensures the most appropriate content is passed to the LLM reducing noise and improving the final response. Reranking is particularly valuable when working with large or diverse document collections.
The LLM receives the query along with the retrieved content and generates a comprehensive response. Any LLM capable of handling extended input can be used, whether it is a commercial model such as GPT-4 or an in-house solution. The primary requirement is the ability to process both the question and supporting information together.
Deploying a RAG system involves several steps, including converting files to text, chunking, vectorising, indexing and generating answers. While various frameworks can assist with these tasks, a fully managed solution can offer significant advantages in efficiency and accuracy.
One example is Tovie Data Agent which connects to internal knowledge bases and a range of data sources. It provides accurate context-aware responses and supports deployment either on-premises or in hybrid cloud environments. This flexibility makes it suitable for organisations with complex data governance needs. Our RAG AI agent can integrate with a broad selection of enterprise data connectors without requiring extensive custom development, offering a strong advantage.
Quality and safety in RAG systems
Because LLMs can sometimes produce incorrect or misleading information, many RAG implementations include additional safeguards. These can include guardrails, moderation tools and LLM response checkers. Such measures are especially important when working with sensitive or proprietary data.
Role-based access controls are often used to prevent unauthorised disclosure, ensuring the system only shares appropriate content. Many businesses also deploy reliability layers to track errors, record activity and evaluate whether the correct documents were retrieved for each query.
To maximise the value of retrieval-augmented generation, companies must prepare their content effectively. Best practices include:
- Using supported formats such as PDF, DOCX, TXT, XML, JSON, and HTML
- Structuring documents clearly with consistent headings and sections
- Keeping information current and removing outdated material
- Avoiding contradictions
- Ensuring formats like JSON, XML, and YAML are valid and logically organised
- Using clean markup for Markdown and HTML files
It is also advisable to develop a test set of questions and expected answers. Running these through the system and scoring responses helps assess accuracy and identify areas for improvement.
Companies are already using RAG across a wide range of scenarios.
Customer-facing chatbots
A RAG AI chatbot can use internal specifications, customer feedback, and product documentation to provide precise answers that general-purpose models lack.
Technical support
RAG helps support teams identify similar past cases and consult internal guidelines, enabling faster resolution of customer issues.
Smart internal assistants
RAG enhances employee productivity by facilitating efficient searches across large datasets. This includes linking the system to CRMs, HR platforms, ERP systems, and industry reports. For example, marketing teams can use it to compile reports from client feedback and web analytics, while HR departments can extract relevant information from CVs.
The key advantage of RAG AI is that it delivers up-to-date responses without requiring constant model retraining. This reduces the risk of hallucinations and saves time and resources.
The future of RAG in 2025 and beyond
Mainstream adoption
Following a surge in generative AI interest during 2024, RAG has become the preferred method for many organisations seeking to integrate their data with LLMs. Recent figures suggest that over half of businesses deploying generative AI now favour RAG-based approaches while the use of fine-tuned models has declined sharply.
Beyond fixing weaknesses
While RAG initially addressed the limitations of static models and hallucinations, it has since become a foundational element in more advanced AI workflows. AI agents that can perform searches, calculations and multi-step reasoning increasingly rely on RAG to access knowledge bases and online information dynamically.
The importance of data quality
The success of RAG depends heavily on the quality of the underlying data. As a result, companies are reassessing their document management practices and investing in tools to organise and cleanse their internal knowledge. Solutions like Tovie Data Agent are helping enterprises centralise their data and ensure it meets the standards required for effective AI integration.
Combining RAG with model training
Looking ahead, many experts believe combining RAG with model training will offer the best results. This layered approach typically includes:
- A general-purpose LLM for broad knowledge
- A domain-specific model trained on industry-relevant data
- RAG for accessing real-time company information
This strategy allows AI systems to produce answers that closely align with the content and tone expected in a given business context.
LLMs augmented by RAG are substantially more useful than models operating in isolation. Research suggests that employees spend significant time searching for the right information each day. RAG LLM has the potential to dramatically reduce this, improving productivity and decision-making.
The RAG ecosystem continues to evolve, with improvements in model databases and deployment frameworks making it easier for organisations to adopt. By transforming static LLMs into live knowledge systems, RAG generative AI is helping businesses unlock new levels of automation insight and competitive advantage.