What is RAG and Why Should Enterprises Care About It?
Large language models (LLMs) have emerged as one of the most transformative workplace technologies, capable of supercharging knowledge discovery and productivity.
Yet, many business leaders have been left frustrated that they have not been able to deliver on their expected value. MIT Sloan reports that only 10% of companies are obtaining significant benefits from AI.
One of the reasons off-the-shelf generative AI (gen AI) tools often fail to live up to the hype is that they are only trained on the information that’s available to them. Businesses are environments that require enterprise-specific knowledge and companies will always struggle to generate strategic advantage with publicly available data.
Retrieval-augmented generation (RAG) optimises the output of a LLM so it references an authoritative knowledge base outside of its training data before generating a response. By combining your data and world knowledge with LLM language skills, responses are more accurate, up-to-date, and relevant to your specific needs.
Why do we need RAG when we have LLMs?
To understand why RAG is important, we have to dive deeper into how LLMs can let you down.
Hallucinations & Inconsistent Results
The most well known risk of LLMs are hallucinations (i.e. when AI creates incorrect information). Additionally, very similar or even identical prompts can generate dramatically different results. Sometimes these results are accurate, sometimes they are not. While it makes sense for an answer to vary based on the specific context of a user, the knowledge contained within the answer should consistently be accurate and complete.
RAG feeds relevant facts directly into the LLM’s prompt, significantly reducing hallucination risks and ensuring outputs remain grounded in factual information. Many solutions also provide citation sources similar to footnotes in research papers so you know exactly where the information is coming from.
Limited Context Window
A model can only take a maximum size of input words for its prompt due to computational contractions. While models like GPT-4o can take up to 128,000 tokens (about 300 pages of text), this is still not enough if you want to answer questions about large volumes of documents or data. Moreover, the more data contained in a prompt, the less accurate the LLM is in processing it and delivering a result - this problem is called the "forgotten middle".
No Access to Proprietary Content
LLMs are trained on publicly available data, but to gain a competitive edge, businesses must leverage their own unique knowledge and expertise.
For instance, when I ask ChatGPT-4o: “What's our latest expense approval workflow and who needs to be notified of recent changes?”
It is not be able to provide the relevant details.
But when I use Quench's RAG solution, it can draw information from all of an organisation’s tools to provide a complete and accurate response.
RAG vs Fine-Tuning
The distinction between RAG and fine-tuning can sometimes be unclear but there are important differences.
Fine-tuning adapts a pre-trained LLM to a specific task by training it on domain-specific data. Whereas, RAG is about providing additional context to the model at the time it is called.
Fine-tuning shines when you need to improve the model’s ability to perform new skills. These include capabilities such as sentiment analysis, question answering, or document summarization with higher accuracy.
However, RAG systems offer compelling advantages for knowledge retrieval:
- Preserved Model Capabilities - Unlike fine-tuning, which can degrade a model’s pre-trained intelligence, RAG keeps the LLM unchanged, ensuring it retains its full reasoning and language abilities.
- Access to Up-to-Date Knowledge - Fine-tuned models are limited to their training data, while RAG dynamically retrieves and integrates the latest enterprise knowledge from documents, databases, and other sources.
- Lower Data & Maintenance Costs - Updating a fine-tuned model requires costly and time-consuming retraining, whereas RAG allows seamless knowledge updates by modifying external sources.
Ultimately, the choice between RAG and fine-tuning depends on your specific needs and it is rarely a binary decision.
There are many instances where these two techniques are needed in combination. For example, a medical diagnostician would need both specialized training (fine-tuning) AND access to the patient's specific test results and medical history (RAG) in order to provide accurate diagnoses and treatment recommendations.
How Does RAG Work?
One of our customers described Quench as “an AI librarian that picks out the right book and points you to the right page, paragraph and sentence – every single time.”
The library analogy is a great way to visualise RAG in action.
With this in mind, let’s look at RAG through its three primary components: pre-processing, retrieval and reasoning.
Pre-processing: Organising the Information
Think of pre-processing as organizing a vast library. Raw information gets transformed so that it can be searched based on the user prompts through several key steps:
- Data chunking: This process involves breaking down large data into smaller, manageable pieces. E.g. you might break down a 100-page manual into different sections which focus on a specific topic.
- Document embeddings: These chunks now need to be converted into numerical representations (vectors) that capture semantic meaning behind words. This allows the system to match user queries with relevant information in the dataset as opposed to just matching keywords.
This preparation creates a searchable knowledge base where information can be quickly and accurately located based on a user’s query.
Retrieval: Finding the Right Information
When a user asks a question:
- The query itself gets converted into the same type of vector representation using identical models to ensure compatibility.
- The system compares this query vector against all document vectors in its database, identifying chunks with the closest semantic similarity.
- These relevant chunks are selected and ranked based on how well they match the user's question.
Think of this as a librarian using an index to quickly locate precisely the right books that contain answers to your specific question.
Reasoning: Crafting the Response
In the final stage:
- The language model combines the original user prompt with the retrieved context to generate a coherent, accurate response.
- One of the challenges here is ensuring that the model’s answers come from the retrieved information rather than making up facts, providing "I don't know" responses when necessary.
- Many RAG systems such as Quench also include citations linking specific parts of answers back to their source chunks.
How to Compare the Accuracy of RAG Solutions?
One of the most important questions for RAG systems in any business is: How accurate are they?
To answer this, we conducted a comprehensive analysis of enterprise search tools, comparing Quench against Sana Labs, NotebookLM, and Google Drive Gemini by running over 300 queries using content from two sample datasets.
Quench’s responses were rated 86.3% correct, significantly outperforming top enterprise search tools (Sana Labs – 48.1%, NotebookLM – 37.0%, Google Drive Gemini – 35.8%)
For a full technical deep dive and complete results, check out our detailed report here.
What makes Quench’s Solution Unique?
Contextual RAG for More Accurate Answers
When you use Quench, every piece of information is enriched with its full context.
In many organizations, knowledge is scattered and lacks proper context. Quench solves this by:
- Structuring raw transcripts and unstructured content into coherent, context-rich segments.
- Ensuring that every citation retrieved includes the necessary background information for a complete, accurate response.
- Prioritizing the most relevant information through intelligent citation ranking.
Query Expansion
We understand what you're really asking, even if your question is phrased differently than the source material. Quench intelligently expands queries to capture variations in phrasing, ensuring more comprehensive search results.
For example, if the original query is: “What profit did Acme make in 2019?”
Quench would generate additional queries such as:
- “How much profit did Acme earn in 2019?”
- “What was Acme's profit in 2019?”
- “Can you provide Acme's earnings for 2019?”
- “What profit figures did Acme report for 2019?”
By broadening the search scope, Quench ensures users receive the most relevant and complete information.
Customization for Enterprise Needs
It is often the case that an organization has acronyms, naming conventions, and terminology that generalist LLMs don't understand.
Names like "Husayn" might be incorrectly transcribed as "Hussein," "Husain," or "Usain" in meeting recordings.
When someone asks "What did Husayn say about our Q2 goals?" standard systems might miss the answer entirely because they don't recognize the name variation.
We therefore allow organisations to generate a custom client dictionary where they can add company-specific terminology.
Quench recognizes that your business is not only another dataset, but a unique environment with its own language.
These capabilities explain why Quench consistently delivers more accurate, contextually complete answers than standard RAG implementations.
RAG in Action: How is Quench Supporting Businesses?
Quench delivers tangible benefits to multiple business functions by making knowledge instantly accessible and actionable. Here's how specific teams are leveraging this technology:
People Teams: Accelerated Employee Onboarding
Quench reduces onboarding time by:
- Providing new hires immediate access to company knowledge
- Offering interactive training that adapts to individual needs
- Decreasing management overhead in the training process
For example, new employees can simply ask "What are our main 5 leadership principles?" and receive an instant response.
Operations Teams: Process Clarity and Compliance
Operations professionals can cut decision-making time in half by:
- Searching across all operational tools and repositories simultaneously
- Finding current processes and policies with complete context
- Accessing verified, up-to-date information with change tracking
A team member can ask "Show me our current vendor approval process and highlight any changes from last quarter" and receive a clear, contextualized response that highlights recent modifications.
Sales Teams: Meeting Preparation and Deal Intelligence
Sales representatives gain competitive advantage through:
- Instant access to prospect and client information before meetings
- Comprehensive deal status updates and history
- Better preparation for client conversations
Before an important call, reps can quickly ask "What's the status of our deal with Google?" and receive a complete briefing with relevant context and next steps.
Want to Try Out Quench?
By delivering contextually rich, accurate information exactly when needed, Quench’s RAG solution empowers teams to work more efficiently and make better decisions across the organization.
If you’re interested in learning more, you can sign-up to get started with Quench.
