In search of value extraction from data
The surging expectation surrounding modernized workflows among businesses has increased market receptiveness towards adopting Generative AI (GenAI). A Gartner survey revealed that 45% of executives have reported a rise in AI investment prompted by the use of ChatGPT.
Amid increased investments and penetration of technologies such as super-resolution, text-to-image conversion, and text-to-video conversions, businesses are embracing GenAI by utilizing underlying AI models like LLMs to unlock various benefits.
However, GenAI models like ChatGPT and Gemini must catch up as enterprises actively seek context-specific solutions in their business strategies. In e-commerce, for instance, companies deploy contextual AI models to enhance customer recommendations. These models can generate personalized product recommendations by understanding individual users’ unique preferences, purchase history, and browsing behavior. Consequently, this has led to the rise of models like Retrieval Augmented Generation (RAG) and Synthetic Data Generation (SDG).
The growing prevalence of RAG and SDG
According to a recent Harvard survey, customer, regulatory, and investor expectations were among the major forces driving the adoption of enterprise GenAI. GenAI can process large volumes of data, identify customer objectives, and deliver faster and more personalized experiences besides unearthing new revenue streams.
However, this alone is no longer sufficient. Futuristic enterprises are taking it to the next level by combining forces with RAG and SDG. Companies gradually realize that processing large datasets is insufficient – they require ‘context-sensitive information retrieval’ to augment decision-making, elevate customer journeys, and boost profitability.
There is also a growing need for ‘artificial data’ that offers robust protection against traditional data privacy and anonymization techniques while preserving its utility. This is where RAG and SDG step in, helping businesses achieve more accurate, efficient, and ethical data-driven operations.
To understand it better, let us look into RAG and SDG’s basic architecture.
RAG: This subset of GenAI comprises the retriever and the generator. The input query goes into an information retrieval component pre-trained on the system’s content. Using Natural Language Processing (NLP), relevant text passages or documents are retrieved based on the key terms. The generator (GPT-based model) processes the retrieved data and provides the appropriate and contextual information.
SDG: This system includes the generator, the discriminator, and the noise-eliminator. The generator develops relevant text passages or documents based on limited input or creates an entirely new data set if the information is unavailable. The discriminator verifies and validates the data, while the noise-eliminator helps mitigate bias and inaccuracies. Artificial yet statistically emulated, SDG mirrors real-world datasets with precision.
As organizations advance their GenAI journey, RAG and SDG models can be viewed as transformative opportunities urging them to calibrate their strategies across research, development, marketing, sales, and customer service areas.
Real-world applications of RAG and SDG
The grounding data used by an LLM is crucial for any business. Adding an information retrieval layer to LLM gives enterprises control over the inputs fed into the system. Conversely, integrating it with synthetic data platforms is instrumental for cases with negligible or inaccurate input. Here are some prominent real-world applications of RAG and SDG to elevate their impact across industries:
RAG Use Cases
- Chatbots and Virtual Assistants: Digital agents can be deployed to offer more contextual, personalized, and specific responses to knowledge-intensive NLP queries. For instance, an engineering services company’s Chief Technology Officer (CTO) can collaborate with the Sustainability Head to align technological innovations with the latest sustainability goals.
They can leverage RAG-powered chatbots, posing natural language queries about the most recent sustainability benchmarks, regulatory updates, and industry best practices. RAG can empower the company to stay abreast of sustainability trends and optimize the synergy between technology and environmental goals.
- Suggestion or Recommendation-based solutions: These offer personalized advice by interpreting user preferences, browsing history, and current consumer trends. For instance, a fashion boutique can redefine shopping experiences through RAG-GPT-powered fashion recommendations.
Suppose a consumer is searching for attires best suited for a destination wedding. In that case, the interface or chatbot can quickly parse through the user’s preferences, browsing history, and current fashion trends and recommend to-the-point ensemble.
- Automated RFP/RFI responses: Users can generate comprehensive response documents for Requests for Proposals (RFPs) or Requests for Information (RFI) with enhanced quality and precision. To exemplify, responding to RFPs and RFIs demands substantial time and resources for the procurement department of any multinational corporation. The company can effortlessly streamline this process, using RAG to input queries into the LLM.
The system will then analyze the questions, retrieve relevant information from internal knowledge bases, and generate comprehensive and contextually accurate responses. Through RAG, the MNC can enhance the efficiency, accuracy, and resource allocation of its procurement operations.
- Industry documentation: RAG can reduce manual efforts by generating accurate and contextually relevant industry documentation. For example, an aerospace company faced with labor and time-intensive documentation can use RAG to streamline the process. Engineers and technical writers input specific industry standards, compliance, or technical specifications queries.
They can leverage their retrieval capabilities to gather accurate and up-to-date information from internal and external sources and draft contextually relevant and precise industry documentation.
SDG Use Cases
- Data privacy: SDG can improve data privacy by creating artificial datasets that maintain statistical characteristics, enabling secure analysis and sharing without compromising sensitive information. For instance, a bank can utilize SDG to develop datasets that closely resemble real-world data, allowing collaborative model development without compromising customer privacy. The models can help employees ensure compliance with data privacy regulations like GDPR while enhancing fraud detection and risk mitigation.
Moreover, the bank can minimize the risk of data breaches through synthetic datasets, thus upholding customer trust.
- Healthcare analytics: Synthetic data can assess healthcare strategies, enhance ML algorithms (e.g., image classification), pre-train models for specific patient groups, and improve infectious disease outbreak predictions in public health models.
- Training models: In cases where the available data cannot cover all possible scenarios, SDG can be used to create and validate ML model pipelines while mitigating biases. For instance, an oncology research center can utilize SDG to generate synthetic samples supported by a diverse dataset of medical images for cancer detection, ensuring representation across demographics, including age, gender, and ethnicity.
SDG can help them enhance the accuracy of diagnostics, provide equitable cancer detection across demographics, and improve sensitivity and specificity, leading to more accurate and reliable cancer diagnoses.
- QA engineering: SDG can be used for quality engineering to ensure the data is accurate, contextual, and valuable for the desired testing. Deploying SDG can help firms simulate diverse testing scenarios, providing robust validation of software applications.
For instance, a fintech company can create synthetic datasets that mimic real-world transactions, enabling comprehensive software for accuracy, security, and performance under various verticals. In addition to accelerated testing, the process reduces dependency on limited real data and enhances the overall quality assurance of financial systems.
With numerous businesses embracing these novel technologies, it is equally vital for them to maximize their potential by addressing concerns surrounding accuracy, variability, and privacy when operating at scale. Joining forces with the right technology partner can help them address such gaps and ensure swift resolutions for complex cases with fewer escalations.
Leading the change
HTC is helping customers generate SDGs by partnering with SDG leaders like Tonic.ai and Synthetic Data Vault (SDV) to create custom healthcare, public sector, and insurance solutions. For instance, HTC recently collaborated with a healthcare provider to improve their fraud detection model. The HTC team leveraged SDG to generate data to train and calibrate the provider’s security model, thus fortifying data security and privacy of critical patient information.
We are also collaborating with Azure, ChatGPT services, and LLMs like OpenLlama to develop and integrate RAG capabilities.
While RAG and SDG are new paradigms in GenAI, enterprises can leverage these capabilities to scale up, accelerate innovation, and nurture long-term growth.
The way forward
In the coming days, RAG and SDG will pave the way for disruptive AI solutions to help businesses redefine efficiency and process excellence – especially across banking, life sciences, retail, and consumer-packaged goods industries. It will only be a matter of time before other enterprises utilize these capabilities for comprehensive value realization in a fast-paced ecosystem.