What is Table Augmented Generation (TAG)?

The following diagram shows a table-augmented generation framework and data flow, from user prompt to LLM response:

Producing precise, dependable, and relevant responses is crucial.

However, TAG also presents certain challenges:

• Enterprise TAG depends on data retrieval across the entire organization. It's essential not only to maintain current and accurate data but also to have precise metadata that accurately describes your data for the TAG framework.
• The data stored in your enterprise systems must meet the data quality for AI standards of compliance, completeness, and freshness. For instance, if you manage 100 million customer records, can you retrieve and integrate all the data relevant to David Smith in under a second?
• Generating insightful contextual prompts requires advanced AI prompt engineering skills, such as chain-of-thought reasoning, to effectively inject the relevant data into the LLM in a manner that yields the most accurate responses.
• To safeguard data privacy and security, LLM access must be restricted to authorized data only. Using the prior example, this means providing access solely to David Smith's data and to no one else's.

In addition to these challenges, organizations have expressed further concerns, as highlighted in our recent survey below:

Top concerns in leveraging enterprise data for GenAI and RAG

Source: K2view Enterprise Data Readiness for GenAI in 2024 report

Given the equal distribution of key concerns, it's clear that focusing solely on one of them – such as scalability and performance, while neglecting data quality and consistency, real-time data integration and access, data governance and compliance, or data security and privacy – will not suffice. To effectively combine enterprise data with GenAI initiatives, a comprehensive, integrated strategy is needed.

"Organizations are continuing to make significant investments in GenAI," says the 2024 Gartner LLM report, Lessons from Generative AI Early Adopters. "But they encounter difficulties related to technical implementation, expenses, and talent acquisition."

Despite these challenges and concerns, TAG represents a significant advancement in GenAI technology. Its capacity to leverage fresh enterprise data addresses the limitations of traditional generative models by enhancing user interactions with more personalized and dependable responses.

TAG is already proving its worth in various sectors, including customer support, IT service management, sales and marketing, and legal and compliance. But the optimal GenAI approach – whether TAG or prompt engineering vs fine-tuning – must be assessed based on the use case at hand.

Enterprises use TAG for multiple domains, including: 

In the realm of generative AI, your data serves as a key competitive advantage. Implementing TAG offers several benefits:

1. Accelerated value realization at reduced expense
   Training an LLM demands significant time and resources. By providing a faster and more cost-effective method for incorporating new data into the LLM, TAG makes generative AI both accessible and dependable for both customer-facing and internal operations.
2. Tailored user interactions
   By combining detailed customer 360 data with the broad general knowledge of the LLM, TAG enables personalized user interactions through chatbots and customer service representatives, delivering real-time, customized recommendations for next-best-actions, cross-selling, and upselling. This allows for scalable AI personalization.
3. Enhanced user confidence
   TAG-powered LLMs deliver credible information by ensuring data accuracy, up-to-dateness, and relevance, all tailored to the individual user. This fosters user trust, which in turn safeguards and enhances your brand's reputation.
4. Better user experiences and lower customer support costs
   Chatbots powered by customer data enhance the user experience and drive down customer care expenses by boosting first-contact resolution rates and reducing the overall volume of service calls.

The TAG life cycle – from data augmentation to generation – is based on a Large Language Model or LLM.

An LLM is a fundamental Machine Learning (ML) model that utilizes deep learning algorithms for natural language processing. It is trained on vast quantities of publicly available text data to comprehend complex language patterns and relationships, enabling it to perform tasks such as text generation, summarization, translation, and question answering.

These models are pre-trained on extensive and diverse datasets to master the nuances of language processing and can be further refined for specific applications or tasks. The term "extensive" barely captures the scale, as these models can encompass billions of data points. For instance, GPT 4.o is reported to contain over a trillion.

TAG implements retrieval and generative models through 4 key stages:

1. Data access
   The initial step in any TAG system involves data access. The retrieval model queries your enterprise data to identify and gather relevant information. To ensure accurate, diverse, and reliable data sourcing, it's crucial to maintain precise and current metadata, as well as manage and minimize data redundancy.
2. Preparing enterprise data for retrieval
   Companies must organize their multi-source data and metadata to enable real-time access by TAG. For example, customer data, including master data (unique customer identifiers), transactional data (e.g., service requests, purchases, payments, and invoices), and interaction data (emails, chats, phone call transcripts), must be integrated, unified, and organized for immediate retrieval. Depending on the use case, data may need to be arranged by other business entities, such as employees, products, suppliers, or any other relevant factor.
3. Data protection
   The TAG framework must implement role-based access controls to prevent users from accessing information beyond their authorized scope. For example, a customer service representative dealing with a specific customer should be restricted from accessing data related to other customers, and a marketing analyst should not have access to certain confidential financial data. And any sensitive data retrieved by TAG, such as Personally Identifiable Information (PII), must be protected from unauthorized access, like credit card details for salespeople or Social Security Numbers for service agents. The TAG solution must employ dynamic data masking to ensure compliance with data privacy regulations.
4. Prompt engineering for enterprise data
   To properly integrate enterprise data, the table-augmented generation process constructs an enriched prompt by synthesizing a "story" from the retrieved 360-degree data. Continuous prompt engineering tuning, ideally supported by Machine Learning (ML) models and advanced chain-of-thought prompting techniques, is essential. For instance, the LLM can be asked to reflect on its existing data and identify necessary additional data or explain why a user terminated a conversation.

The need for TAG to access enterprise application data in real time can clearly be seen in customer service chatbot use cases, where immediate responses are required.

The data needed for generative AI use cases in customer service is typically found in CRM, billing, and ticketing systems.

A real-time, comprehensive understanding of the relevant business entity, be they customers, employees, suppliers, products, or loans, is essential.

However, the procedure for locating, accessing, integrating, and unifying real-time enterprise data, and then integrating it with your LLM, is very intricate due to:

• Fragmented enterprise data dispersed among many different systems, each with its distinct databases, structures, and formats.
• High complexity and cost of data management required for real-time retrieval of the data pertaining to a single entity (such as an individual customer).
• Performance issues arising from high-volume and low-latency demands.
• Consistently delivery of clean, complete, and up-to-date data.
• Privacy and security mandates that limit access to authorized data only.
• AI database schema generator for cross-system data visibility.

Numerous organizations are currently testing TAG chatbots with internal users, such as customer service agents, as they remain cautious about deploying unsupervised systems in production environments. These concerns are centered around generative AI hallucinations, privacy, and security. As GenAI systems demonstrate increased reliability, this trend is expected to shift.

TAG apps are rapidly emerging across various sectors. Examples might include:

1. Car owners requesting claim histories to reduce annual premiums

2. Hospital patients wanting to cross-reference the treatments they received to the payments they made

3. Stock investors wishing to see the commissions they were charged year over year

4. Telco subscribers selecting new plans since theirs are about to terminate

TAG is currently being used to deliver precise, context-aware, and up-to-date answers to inquiries through chatbots, email, text messaging, and other conversational AI applications. In the future, TAG might propose suitable actions based on contextual information and user prompts.

For example, while today GenAI might be used to provide army veterans with information about higher education reimbursement policies, it could eventually list nearby colleges and even recommend programs tailored to the applicant's prior experience and military training. It might even be capable of automatically generating the reimbursement request itself.

At the end of the day, it’s hard to deliver strategic value from GenAI today because your LLM lacks business context, costs too much to retrain, and hallucinates too frequently. What's needed is LLM grounding on a consistent basis.

K2view GenAI Data Fusion is a patented table-augmented generation solution that grounds your generative AI apps with real-time enterprise data from any source.

With GenAI Data Fusion, your data is:

1. Ready for GenAI
2. Deliverable in real time and at enterprise scale
3. Complete and fresh
4. Compliant and trusted

The diagram shows how the K2view Data Product Platform and Micro-Database™ technology enable GenAI Data Fusion to access structured data:

1. The user enters a prompt, triggering GenAI Data Fusion to access your structured enterprise data.

2. Micro-Database technology queries only the relevant business entity (e.g., specific customer). 

3. The original prompt is augmented with the entity's data, metadata, and context to create intelligent prompts for the LLM.  

4. The LLM uses the augmented prompt to generate a personalized and reliable response to the user.

Supercharging TAG with enterprise data

K2view GenAI Data Fusion organizes your fragmented enterprise data into 360° views of your business entities, making it instantly accessible.

Each 360° view is stored in its own secure Micro-Database, 90% compressed and built for low-footprint, low-latency TAG access.

K2view organizes the data for each business entity in its own high-performance Micro-Database – always ready for real-time access by TAG workflows.

By continually syncing with underlying sources, Micro-Databases can be modeled to capture any business entity – and comes with built-in data access controls and dynamic data masking capabilities.  

Micro-Database technology features:

• Real-time data access, for any business entity

• 360° views of any entity

• Fresh data all the time, in sync with underlying systems

• Role-based access controls (RBAC), to maintain data privacy

• Lower TCO, thanks to a small footprint and the use of commodity hardware

An entity’s data can be queried by, and injected into, your LLM as a contextual prompt in milliseconds. Entity-based TAG assures AI data readiness.

GenAI Data Fusion can:

1. Retrieve and augment structured data about a customer or any other entity.

2. Dynamically mask PII (Personally Identifiable Information) or other sensitive data.

3. Be reused to handle data service access requests, or suggest cross-sell recommendations.

4. Access enterprise systems via API, CDC, messaging, streaming – in any combination – to aggregate data from multiple source systems.

It powers your GenAI apps to respond with accurate recommendations, information, and content – and without LLM hallucination issues – for a wide range of use cases, such as:

Learn more about the K2view GenAI Data Fusion TAG tool.