Businesses are currently facing a critical challenge: how to leverage AI systems that deliver precise, relevant, and actionable insights without compromising data privacy. While the large language models (LLMs) most frequently adopted in business environments, such as OpenAI's GPT or Meta's Llama, are powerful, they are trained on vast swathes of external knowledge and data spanning the entire internet and are often not suitable for niche or business-specific tasks. Successfully integrating AI technologies into everyday workflows requires models to have specific knowledge about your business.  

Retrieval augmented generation bridges the gap between general AI capabilities and specific business needs by combining retrieval-based systems with generative AI. It provides access to domain-specific, up to date information stored in private datasets, ensuring responses are accurate, relevant, and free from hallucinations. Moreover, RAG supports businesses in maintaining data privacy and operational efficiency, aligning seamlessly with the need for tailored, secure, and sustainable AI solutions.  

Understanding RAG: What Is It and How Does It Work?

Retrieval-Augmented Generation (RAG) is a technique in natural language processing (NLP) that enhances the performance of LLMs by integrating external knowledge sources. Unlike traditional LLMs, which rely solely on pre-trained datasets, RAG incorporates business-specific data to produce responses tailored to unique business needs. This dual approach ensures RAG combines the contextual relevance of retrieval systems with the creative capabilities of generative models.  

A RAG system first involves retrieving relevant information from a dataset provided by the user, before generating a response. The system can cross-reference the user’s documents with the question posed by the user and ultimately return an answer that is best matched to the user query. This means that the generative AI model can reason with the provided documents in order to provide the most relevant answer for the user.  

To get more technical, a RAG system uses embedding models to take documents of any form  (powerpoints, PDFs, texts, etc.) and ingest them into a vector database where they are parsed for similar meanings using semantic similarity, which is a technique which involves finding words that are similar in meaning, even if the words themselves are not syntactically similar.  

Once the text is in the vector database, the user can ask questions based on the corpus of documents that have been ingested. The user query is embedded into the same vector space as the documents, and the vector database returns the documents - or pieces of documents - which best match the user query in actual meaning, rather than keyword matching. Finally, the context and the user query are passed to the large language model which will synthesise an answer.  

The RAG Pipeline

To understand what RAG is, it is important to understand the components that make up a RAG system. A RAG system consists of multiple components, combining the strengths of retrieval-based systems, which find relevant data, with generative models that create coherent and contextually appropriate text based on training data.  

1. Ingestion Pipeline: Responsible for preparing and embedding documents into a retrievable format.  

2. Retrieval Pipeline: Ensures the system finds and prioritises the most relevant information for a given query.  

3. Synthesis Pipeline: Generates a final response by leveraging retrieved data and LLM capabilities.  

The Ingestion Pipeline

The ingestion pipeline is the crucial first step in a retrieval augmented generation system that is responsible for preparing relevant documents so they can be effectively parsed, broken down, and embedded for usage by the system.  

1. Parsing Documents: The pipeline extracts relevant information from diverse formats (e.g., PDFs, PowerPoints) and converts it into structured text. This involves cleaning raw data, removing unnecessary elements, and extracting metadata for indexing.  

2. Chunking for Efficiency: Documents are broken into smaller segments, or "chunks," to facilitate quicker and more accurate processing. These chunks are designed to balance granularity and context, enabling seamless retrieval.  

3. Embedding with Semantic Meaning: Each chunk is processed through an embedding model that converts it into a vector representation, capturing its semantic meaning. This step ensures the system understands not just the words but the context behind them.  

By embedding documents into a vector space, RAG systems make it possible to query the database using semantic similarity, rather than traditional keyword matching, for highly relevant results.  

The Retrieval Pipeline

The retrieval pipeline kicks in when a user asks the model a question. It’s all about finding the best answer and is responsible for retrieving the chunks of information that are most relevant and match the user query.  

1. Embedding User Queries: The query is embedded in the same vector space as the documents, enabling a semantic match. Techniques like Cosine Similarity help rank the documents based on relevance.  

2. Optimising Results: If multiple matches are found, the system performs post-processing to refine the results. This could involve adding metadata or expanding the context with surrounding text to enhance relevance.  

3. Delivering Key Context: The pipeline ensures the most critical and contextually rich data is passed to the synthesis pipeline.  

This process ensures that businesses receive accurate insights tailored to their unique requirements.  

The Synthesis Pipeline

The final component of a RAG system is the synthesis pipeline, which is responsible for integrating the retrieved information and generating a coherent response. The purpose of this stage is to leverage both the retrieval results and the generative capabilities of a language model to produce the final output, while also ensuring that the LLM does not hallucinate or provide inaccurate responses.  

1. Contextualising Input: Retrieved data and the user query are concatenated into a single input, formatted, and tokenised for processing by the LLM. The model is also instructed to refrain from answering if the context lacks sufficient information, eliminating hallucinations.  

2. Generating Responses: The LLM synthesises the input, using its reasoning capabilities to provide clear, precise answers. The pipeline ensures responses are traceable, linking back to the original data source.  

This combination of retrieval and generation delivers results that are not only accurate but also explainable and verifiable, meeting the stringent demands of modern businesses.  

Why RAG Outshines Fine-Tuning

When it comes to improving the accuracy of models’ responses, fine tuning and RAG are both popular approaches. RAG is often the preferred method for optimising the performance of LLMs, however they can also be used alongside each other for optimal results.  

LLM fine tuning is an effective technique for improving the capabilities of existing models, and is most effective on narrow tasks, such as summarisation or asking a model to respond in a certain tone. Fine tuning is the process of taking a pre-existing and pre-trained LLM, and training it further on a specific dataset, for example your business documents. However, while the fine-tuned model has access to information relevant to the user query, it still runs the risk of hallucination, as it is also pre-trained on vast quantities of training data.    

Contrastingly, a RAG system almost completely diminishes the risk of hallucinations as the user can guarantee that the answer to their query is in the dataset provided to the model. And if the RAG cannot answer the user’s question, it simply will not. Unlike a fine-tuned model, a RAG system can document its lineage, in other words, precisely from where it has sourced its responses within the knowledge base. Therefore, when optimising LLMs for business-specific tasks, RAG offers distinct advantages.  

What are the key benefits of RAG for a business?

RAG helps businesses mitigate the risks associated with LLMs by ensuring greater accuracy, contextual relevance, compliance, efficient use of knowledge, and reduction in hallucinations.  

• Mitigating Hallucinations: By grounding responses in retrievable data, RAG minimises the risk of inaccurate or misleading outputs.  

• Ensuring Contextual Relevance: Responses are tailored to business-specific queries, leveraging private datasets for unmatched accuracy.  

• Maintaining Compliance: Proprietary data remains secure and is not used for model training, ensuring alignment with stringent privacy regulations.  

• Enhancing Efficiency: RAG optimises knowledge utilisation, helping businesses reduce redundancy and improve decision-making.  

Clairo RAG

Clairo AI’s existence is defined by an unwavering commitment to data-privacy, environmental sustainability, and digital innovation. Clairo RAG allows us to uphold those values and allow our users to leverage the power of Large Language Models without compromising the security of their data, straining their budget, or impacting the environment.  

RAG and Data Privacy

Businesses should be able to harness the power of generative AI models without compromising the integrity and privacy of their data. Traditional generative AI solutions rely on processing vast amounts of user-inputted personal data to continuously learn and improve, putting the privacy of data at risk. As a result, businesses are increasingly prioritising data privacy in their AI strategies. In contrast, proprietary data is not used to train or fine-tune models in RAG systems, ensuring that companies can confidently protect their sensitive information.  

RAG and Environmental Sustainability

At Clairo AI, we are on a mission to combat the environmental impact of AI solutions, and RAG is a core driver of this mission. By leveraging existing databases to retrieve relevant context, RAG systems reduce the need for computationally intensive data processing and therefore lower energy consumption compared to traditional Generative AI solutions. As well as reduced data processing and response times, RAG systems use retrieval mechanisms to find and incorporate relevant data on-demand, and do not require the same level of exhaustive training, thus leading to lower energy use.  

Clairo RAG in Action: Elevating AI Agents

RAG's ability to combine retrieval and generation makes it a powerful tool for enhancing AI agents. These autonomous systems rely on relevant, private data to navigate complex tasks, such as customer support, data analysis, and operational decision-making.

With Clairo AI, users can build custom AI Agents that are tailored to a range of specific business use cases with privately-held data retrieved through RAG, allowing them to navigate complex tasks and decisions.

Retrieval-Augmented Generation represents a game-changing solution for businesses seeking AI systems that are precise, relevant, and secure. By seamlessly combining the power of retrieval-based systems with the creative capabilities of generative AI, RAG delivers insights that are both contextually accurate and grounded in private, proprietary data. This innovative approach not only eliminates risks like hallucinations but also supports compliance with data privacy regulations, ensuring businesses can trust their AI outputs.

With Clairo AI’s RAG solutions, businesses can unlock the full potential of AI while upholding the highest standards of privacy and environmental responsibility. For organisations navigating the demands of modern operations, RAG is the key to staying ahead—delivering smarter, faster, and more secure insights.