Supercharge productivity: 5 retrieval-augmented generation use cases

Generative Artificial Intelligence (GenAI) has the remarkable ability to generate various types of content based on the data available from its training period. However, there are instances where we witness these models assert something completely nonsensical with unwavering certainty. Such “hallucinations" typically occur when the models lack the necessary context to respond accurately to a query.

These erroneous assertions can be surprisingly persuasive and are often indistinguishable from factual statements. Because of the model’s confident presentation, users might be inclined to trust almost any information it generates. This poses a significant challenge for users who rely on GenAI tools, as discerning fact from fiction becomes increasingly difficult.  

Another issue is that many large language models (LLMs) essentially have a “frozen-in-time" mentality, lacking the capability to access or incorporate the latest information. These models are initially trained on a vast corpus of data. However, this data can quickly become outdated and irrelevant to every user's needs. Retrieval-augmented generation (RAG) offers a solution to this problem by allowing users to input up-to-date data, thus providing the LLM with the necessary context to produce relevant and current responses.

RAG can positively impact productivity across different business functions. Let's dive into 5 RAG use cases that aren't just enhancing our capabilities—they're redefining what's possible.

How to use retrieval augmented generation: 5 use cases

1. Enhanced content crafting with RAG 

The RAG technique can significantly streamline and improve content creation to make it tailored and more specific to the user’s needs. You can ask a few LLMs questions without providing additional context to help with content creation, but you will find that the quality greatly increases after providing relevant and well-labeled data to the LLM. This will streamline the user experience by generating improved initial drafts, summaries, or even complete articles with the most relevant and up-to-date data. 

It does so by sifting through the data provided as context to find the most relevant information, which it then uses to compose content that aligns with the intended messaging and audience interests. This goes for other multimodal models, such as text-to-image and text-video, where users can provide images as benchmarks or examples to the model.

2. RAG-driven market intelligence

Approaching market research with the RAG technique significantly enhances the process by integrating the strengths of web search engines and LLMs. Utilizing the RAG technique, LLMs can use comprehensive external knowledge databases, enabling a more streamlined and strategic analysis of large data sets. This technique allows for the generation of recommendations, detailed reporting, and data interaction in a conversational interface. A great way to review the output is by requesting citations on the data provided; this allows an easy way to check the credibility of the results.  

This powerful combination leads to a high level of data gathering and insight creation. It helps analysts simplify and make sense of complex information from various sources. Utilizing the RAG technique, companies can quickly identify trends, understand competitors’ activities based on publicly available information, and gauge market changes.

As this approach goes through up-to-date available market data, social media sentiment, and industry reports, it can help provide businesses a crystal-clear picture of where they stand against others, helping them to make well-informed, sharp decisions. In short, RAG is changing the game in competitive analysis by making market research more thorough and insightful, giving businesses the tools they need to move with speed and vision in a market that's always changing.

3. Chat with data

The integration of the RAG technique with GenAI is bringing in a new way of interacting with data, where conversing with complex databases becomes as simple and intuitive as chatting with a knowledgeable friend. And this friend won’t be frustrated at how many questions it’s getting asked.

With RAG’s data retrieval capabilities and LLMs’ natural language processing, users can engage in data-driven dialogues that go beyond basic information retrieval. This approach allows the LLM to generate responses that are contextually relevant and supported by the most current and accurate data sourced. The use of APIs can provide the pipelines for fresh data to flow into the model, ensuring that every conversation is supported by the most current and comprehensive information available. With this level of augmentation, the model can return concise summaries or detailed reports as if they were part of a natural back-and-forth conversation. This empowers decision-makers to extract insights and take action without ever having to write a line of code or run a complex query.  

4. Improve customer engagement

Understanding customers at an individual level is key to revolutionizing engagement. While customers interact with companies through various channels such as events, emails, social media, websites, and advertisements, these channels are often siloed within large organizations. Consequently, companies may only have access to fragmented insights into a customer's interests. It's also common for interests to evolve, and customers might not update their profiles with the latest changes.

By employing a RAG technique, companies can integrate data from multiple interaction points via APIs (while respecting privacy and consent) and use machine learning to help with clustering interest. This allows for a more holistic assessment of a customer's behavior, enabling the user with insights and tailored recommendations based on a comprehensive, up-to-date view of their interests. Such a dynamic approach can significantly enhance customer engagement by providing personalized experiences that resonate with the ever-changing preferences of the audience.

5. Internal and external education 

Small and large companies can facilitate swift and effective education to bring new employees up to speed or help customers understand products and services. Some methods often involve fine-tuning a LLM with company data which can quickly become outdated. The RAG technique addresses this limitation by allowing users to provide the most recent data, ensuring that the information provided is always current.

Internally, the RAG technique can be a game-changer for onboarding new hires by providing instant access to an intelligent assistant that answers questions in real time, directs them to the right resources, and allows updates to its knowledge base with the latest company information. This accelerates the training process and fosters an environment of self-sufficiency and continuous learning, allowing employees to discover all that’s available to them. 

Externally, the RAG technique can be leveraged to help educate customers effectively. By offering a conversational interface, potential customers can engage in a dialogue that feels as natural and informative as talking to a seasoned sales agent. Moreover, when integrated with APIs, the system can utilize data such as recent user interactions (while respecting privacy and consent) to tailor the conversation, creating a more personalized and engaging customer experience.

Areas to consider when implementing RAG 

Implementing RAG requires careful consideration of several factors to ensure its effectiveness and compliance with regulatory standards. The quality of the RAG system's output is directly linked to the quality and structure of the data provided, emphasizing the need for high-quality, well-organized data inputs.  

Additionally, data classification becomes critical as it determines how data will be interpreted and utilized by the company and LLM. Proper classification ensures that the data is used responsibly and adheres to regulations accordingly.  

Data privacy and security are paramount when dealing with sensitive information. Implementing RAG must be aligned with data protection laws and ethical guidelines to safeguard user data against misuse and breaches.

Lastly, the use of RAG carries individual responsibility. Users must stay informed about the implications of AI technology and adhere to best practices in data management and legal compliance. Understanding these responsibilities is essential for maintaining trust and integrity when leveraging these technologies and techniques. 

For additional information about Outshift’s role in the global GenAI discussion, read more on why we care about trustworthy and responsible AI