AI hallucination is one of the biggest concerns in generative AI adoption in enterprises. Generative AI finds potential use cases in every industry. However, the underlying large language models require a deep understanding of the business context and domain knowledge to perform specific tasks. This is where the retrieval-augmented generation framework emerges as a superpower. In this blog, we will discuss what LLM RAG is and what you should know before implementing this framework.
What is LLM RAG?
Retrieval-Augmented Generation is a framework in generative AI to give large language models the ability to generate more accurate and relevant responses from your business data.
In this framework, you combine a model with your business-specific datasets or domain-specific knowledge bases. When you ask the generative AI application a question, the system retrieves the latest, relevant data from the connected sources and guides the models to generate accurate responses.
Can’t the large language models alone do the work, you ask?
LLMs are trained on large volumes of publicly available data, such as books, news articles, etc., to understand the language patterns. By training on the large data sets, the models get the ability to generate coherent responses in any language. But their knowledge is limited by data available only till the cutoff date.
For example, ChatGPT doesn’t have knowledge of events that occurred after September 2021.
Also, the large language models are trained only on publicly available data and their knowledge is not specific to any specific domain or business. If you ask ChatGPT questions specific to your enterprise, it cannot answer. Or it hallucinates and generates random response, which is absolute gibberish.
Unless you are an individual user using generative AI applications like ChatGPT to perform general, regular tasks, the models alone won’t work for mission-critical business tasks.
To ground the models on your business data, there are a variety of frameworks, such as fine-tuning, available. Fine-tuning is the process of training a pretrained model on your business-specific datasets or domains.
Is RAG better than LLM fine-tuning?
RAG doesn’t customize a model but enables it to generate responses based on the latest available data. So, the responses are more accurate. Fine-tuning customizes the model to perform domain-specific tasks exceptionally well. However, it requires intensive resources such as labeled data and high computation power. These resources might incur high costs.
RAG and LLM fine-tuning are not mutually exclusive frameworks. In fact, the combination of these two will complement each other. Fine-tuning brings domain expertise and RAG brings fresh data. Together, they generate more coherent and accurate responses to help you enhance operational efficiency and boost employee productivity.
What are the use cases for retrieval augmented generation?
In any use case where you would need to generate responses based on dynamic data, you can leverage the framework. Let’s discuss a few of the potential use cases in enterprises.
Cognitive enterprise search
Cognitive enterprise search goes beyond keywords and meta information. Traditional enterprise search solutions lack the context and struggle to search through unstructured data. RAG LLMs in cognitive search solutions help you find the latest information from your internal document and knowledge bases. Structured or unstructured, the framework retrieves the latest data from your databases and LLMs answer your queries more accurately.
Conversational AI bots
Your customers want quick and accurate responses to their queries. What else could be a better choice than implementing conversational AI bots using RAG LLMs. These bots can understand the context and retrieve relevant information from your knowledgebase. Then, they can generate personalized responses to help customers resolve the queries.
Real-time risk analysis
Risk assessment with real-time data is essential while making investment decisions. Real-time is the key here. That is where the framework comes into picture in risk analysis. By analyzing company financial reports, real-time market conditions, and regulatory updates, RAG LLMs can help you perform risk analysis with greater accuracy.
Inventory management
Inventory data is very dynamic. Having a real-time view of the information is essential to make better decisions to control overstocking or understocking. Grounding your generative AI solution on the inventory data using the framework helps you generate inventory reports and insights more accurately. You can make the right decisions to keep your inventory levels optimized.
What are the benefits of RAG LLM?
There are some shortcomings with AI models such as hallucinations and inaccurate responses. Combining RAG with LLM will address these challenges and helps you leverage the power of generative AI to the fullest. Let’s see the benefits of using the framework.
Up-to-date, accurate responses: Large language models are only trained on a limited set of data with a cutoff date. With RAG, LLMs can be grounded on dynamic data sets and provide up-to-date, accurate responses.
Controlling hallucinations: Hallucinations is a big problem with generative AI. The models start to hallucinate when they don’t have domain-specific knowledge. Because they are trained on general, publicly available data. With RAG, you can ground the models on your business-specific datasets and enable the model to generate domain-specific, accurate responses.
Cost effective: To implement generative AI solutions for your business, you have several options to prepare the models, such as prompt engineering, finetuning, and pretraining. While prompt engineering is very economic, it’s insufficient for mission-critical business operations that require domain expertise. Fine tuning and pretraining offer granular level control but are cost intensive. RAG LLMs hit the chord by being effective in terms of performance and cost.
What are the challenges with LLM RAG applications?
While implementing the framework, you might face some challenges.
Latency: the gen AI solutions retrieve data from dynamic data sets and generate responses. The real-time data retrieval might introduce some latency in generating responses. Latency in these applications also depends on several other factors such as the token limit of the model, size of the documents, complexity of the query, etc.
Data privacy: The applications are grounded on external data sources, such as your internal documents hub or knowledge base, to generate responses. These data sources might contain sensitive information which only authorized users should access. While grounding on such data sources, you have to make sure that you implemented proper data handling mechanism. The RAG solution should comply with regulatory requirements to ensure data privacy and security.
Irrelevance: The key purpose of these gen AI applications is to generate highly relevant responses based on fresh data. You need to maintain the connected data sources with up-to-date information. Outdated or inaccurate information in the database would result in inaccurate and irrelevant responses.
Costs: While the framework seems to be a cost-effective option compared to fine-tuning and pretraining, you have to acknowledge the nuances, such as infrastructure, data storage, and continuous data management. Cost and performance management should be incorporated in your strategy from the planning stage. Otherwise, the costs of individual components might spiral out and overall returns might not be justified.
Want expert help with generative AI applications?
When done right, the advanced gen AI applications can be a powerful addition to your workforce. You can streamline your business processes that require acting on dynamic data and strengthen decision-making with real-time insights. However, like any other technology, implementing this framework also requires a strategic roadmap for successful implementation and continuous innovation. This is where we at Saxon AI can help you.
Our partnership with data and AI platform providers, such as Microsoft and Databricks, gives us an advantage to leverage the latest innovations and best practices for our customers.
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