Large Language Models (LLMs) have taken the world by storm, demonstrating incredible abilities to generate human-like 'text', translate languages, write different kinds of creative content, and answer your questions in a personalised and informative way. However, these impressive models have limitations. While they are trained on massive datasets, their knowledge is often outdated, and they lack access to private or specialised information. This is where Retrieval-Augmented Generation (RAG) comes in.
What is Retrieval-Augmented Generation (RAG)?
Imagine you have a super-smart assistant who can answer your questions but only knows what it's been taught. Now, picture that assistant having access to a vast library containing all the information you need. That’s the core idea behind RAG. It essentially bridges the gap between LLMs and your specific data, enhancing their capabilities with real-time information retrieval.
How does it work?
The RAG process begins by ingesting your data, whether it's internal documents, product manuals, customer support records, or even specialised databases. This data is then broken down into smaller chunks and converted into numerical representations, known as 'embeddings'. These embeddings allow the system to efficiently search for relevant information based on the meaning of your query, rather than just matching keywords.
When you ask a question, the system searches your data for the most relevant chunks based on their embeddings. These relevant chunks are then combined with your query and presented to the LLM. The LLM uses this contextual information to generate a more accurate, informative, and most important of all, a “grounded” response.
Benefits of RAG
RAG offers several key advantages for businesses looking to leverage the power of LLMs:
•Improved Accuracy: By incorporating real-time data, RAG helps LLMs generate responses that are more accurate and less likely to hallucinate (invent false information).
•Access to Specialised Knowledge: RAG allows LLMs to tap into proprietary data, internal documentation, and other specialised sources of information. This opens up possibilities for a wider range of applications.
•Real-Time Data Integration: RAG can access up-to-date data, ensuring the LLM has the latest information available.
•Explainability: RAG can provide citations for the information it uses, making it easier to understand where the LLM's response came from
Challenges of RAG
While RAG holds immense potential, it's important to be aware of its challenges:
•Data Preparation: Building a comprehensive and well-organised knowledge base requires significant effort, including data pulling, cleaning, chunking, and embedding and other processing.
•Retrieval System Complexity: Designing an efficient search & retrieval system that can accurately find relevant information from a large knowledge base is complex, time-consuming and requires constant maintenance as your data corpus grows.
•Need for Expert Talent: Implementing in-house RAG requires developers with significant knowledge and expertise in the domains of MLOps, DevOps, systems engineering and AI - talent that is scarce and expensive.
•Continued investment to keep up with the latest advances in LLMs and search/RAG.
RAG vs. Fine-Tuning
Fine-tuning is another method to adapt LLMs to specific tasks. While it does help with adapting the LLM to perform a new task (like learning how to classify a tweet into positive, negative, or neutral sentiment), it’s not as good in learning new information from your data. At the same time, it involves retraining the entire model on your data, which can be expensive, time-consuming, and risky. RAG offers a more flexible and cost-effective approach:
Overfitting and a tendency to forget information: when you fine-tune on a specific dataset, there’s a risk that the model will “memorise” the smaller dataset rather than “understand” it. A related risk called catastrophic forgetting is that at the fine-tuning stage the model can forget tasks it previously knew how to solve in favour of new ones.
Hallucinations: one of the key issues of LLMs is hallucinations. When you fine-tune a base model with new data, even if it integrates this new data without overfitting, the issue of hallucinations remains a key challenge for the fine-tuned model.
No Explainability: In the same way it’s hard to explain the outputs of a general LLM like Llama or GPT, it’s as hard to explain the outputs of a fine-tuned model. With RAG, part of the process includes providing references/citations from the retrieved facts that help explain the output of the RAG pipeline.
Requires MLE expertise: fine-tuning involves continued training of the model with a new (often smaller) dataset. It does require significant expertise in deep learning and transformer models to get right.
High Cost: Fine-tuning is expensive and relatively slow. For example, if your data changes every day, you still might not want to fine-tune the model everyday as the costs would quickly rack up.
No Access Control: in RAG, the set of relevant facts is retrieved from the source documents and included in real-time in the LLM prompt. Because of this process, it is easy to also apply access controls. For example, if one of the facts comes from a document that an employee does not have access to, it can be removed from the set of facts before those are sent to the LLM. This is impossible to do with fine-tuning.
Data Privacy: when you fine-tune an LLM with your data, all the data included in the dataset you use for fine-tuning is integrated into the output model as part of its “model weights”, including any confidential information or intellectual property you own. It’s impossible to separate out the confidential from the non-confidential data – it’s just a single updated set of weights. With RAG, similar to how access control works, you have fine control over what facts are used in the process.
Real-World Applications of RAG
RAG is transforming how businesses across industries use AI:
•Customer Support: Chatbots powered by RAG can access internal knowledge bases to answer customer questions more accurately and efficiently.
•Workplace assistants: Assistants and chatbots powered by RAG over company knowledge can help employees in their everyday knowledge tasks like coding and fixing bugs, responding to emails and RFPs, making marketing collateral grounded to company standards, etc.
•Financial Analysis: RAG can help financial analysts analyse regulatory documents, research reports, and market data to make informed investment decisions.
•Legal Research: Legal professionals can use RAG to quickly research case law, statutes, and legal precedents.
•Medical Research: RAG can assist medical researchers in analysing medical literature and clinical trial data.
•Improved web search: Integrating LLMs with web search can greatly improve the user experience over traditional web search “collection of links” paradigm - you can now generate a wikipedia-style page specifically to answer your particular question, while being grounded to reputable and verified information from across the web. Startups like Perplexity have demonstrated the usefulness of this approach as compared to Google search
Simplifying RAG with WitHub
WitHub is a revolutionary AI development platform that makes it incredibly easy to implement RAG for your business. WitHub simplifies the process, providing:
•Managed Toolbox: WitHub offers a comprehensive set of APIs and services, allowing you to easily integrate RAG into your applications without the need for extensive in-house expertise.
•Multimodal Support: WitHub works with various data & file formats, and can even handle images, audio, and video using specialised AI models.
•Easy Integration: WitHub seamlessly integrates with your existing SaaS applications and data sources, making it easy to get started
Conclusion
RAG has quickly become the gold-standard method to implement enterprise applications powered by large language models. By overcoming inherent LLM shortcomings, it brings unparalleled accuracy, contextual awareness, and access to specialised data.
However, implementing production-grade RAG yourself requires a significant upfront investment in time and cost, and keeping up with the latest innovations is also not easy.
By providing a managed “RAG toolbox”, WitHub makes advanced, production-grade RAG accessible to businesses of all sizes. Instead of struggling with the complexities of setting up and managing your own RAG system, WitHub empowers you to focus on what matters most: accelerating your GenAI adoption roadmap and unlocking real business value in the least amount of time.
