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From Large Data to Great Wisdom: How Industry LLMs Elevate Insights, Conversations, and Customer Experience

On August 24, 2023 | 7 Minutes Read
Conversational EngagementGeneric

Is your organization in possession of the latest currency? In today’s world, the most powerful currency is data. An organization seeking to succeed in this world will need to be on the bleeding edge of using data as it provides consistently actionable insights. But not all data is meaningful when surfacing insights that are specific to the boundaries of one particular organization operating in one particular industry.

Enter Industry-specific LLMs. These LLMs are capable of storing large quantities of data contextually relevant to an industry and enabling AI applications to use this data to provide pointed insights, reshape conversations, and revolutionize the customer experience.

What is the difference between Foundation and Industry LLMs?

Foundation Large Language Model (LLM) refers to a general-purpose language model, like GPT-4, that is trained on a wide range of text from the internet to generate human-like text across various topics.

On the other hand, Industry-Specific LLMs are specialized versions of these models that are fine-tuned on data from a specific industry, domain, or topic.  The focus that an industry LLM brings due to the specialized nature of its data propels organizations ahead by improving personalization for their customer, providing data-driven outputs that are industry-specific and without extra data noise, and is even faithful to an industry and an organization’s jargon and terminology.

Limitations of Foundation LLMs:

  • Lack of Domain Expertise: Foundation LLMs lack domain-specific expertise.  The output from Foundation LLMs draws on a great breadth of knowledge as a result of which the subtleties of a particular domain is lost. They might generate plausible-sounding text but can miss the nuances and specifics required in specialized fields. This could mean incomplete or inaccurate information is surfaced to the user.
  • Absence of Specific Vocabulary and Jargon: Why do we go to a tennis coach to learn tennis and not a badminton coach? They both involve rackets and the objective of both games is fairly the same. The subtleties come from the fact that there are words that describe the game such as “fault” and “deuce” in tennis but not in badminton. Most fields have their own unique vocabulary and jargon. Foundation LLMs might struggle to use industry-specific terminology correctly and consistently or might provide explanations that are overly simplified.
  • Inaccurate or Outdated Information: Since Foundation LLMs are trained on a wide range of internet text, they might generate content that is drawn from inaccurate, obsolete, unverified, and generally unsuitable sources for specific industries. This can be problematic, especially in fields where accuracy is crucial and industry updates are frequent, such as medicine or law. After all, we wouldn’t want to go to court attempting to win a case based on a strategy from a movie. 
  • Less Efficient Conversations: Conversations with Foundation LLMs might need more probing to be effective in discussing industry-specific topics. This can make interactions less efficient and more repetitive compared to Industry-Specific LLMs that are already primed with the necessary domain knowledge.

Advantages of Industry-Specific LLMs:

  • Domain Expertise: Industry-specific LLMs are fine-tuned on industry-specific data, giving them a better grasp of domain-specific concepts, terminology, and context. This expertise leads to more accurate and relevant outputs.
  • Precise Responses: These models can provide detailed and precise responses that align with the expectations of professionals in the industry. This reduces the need for repeated and revised prompting to ensure that the necessary answer is provided and also lowers incorrect information from being provided to the user. 
  • Efficient Conversations: Industry-specific LLMs require less context and background information during conversations within their specific domain. This can make interactions more streamlined and effective.
  • Customization: Organizations can fine-tune Industry-Specific LLMs to suit their specific needs. This allows for tailoring the model’s behavior to align closely with the organization’s goals and requirements.
  • Increased Trust: When an Industry-Specific LLM generates accurate and relevant content in a specific domain, it fosters trust among users, as they perceive the model as a knowledgeable and reliable resource.

While Foundation LLMs have their merits in generating general text across a wide array of topics, Industry-Specific ACE LLMs built atop Foundation models offer a more focused and accurate approach when dealing with specialized domains.

In essence, transitioning from Foundation LLMs to industry-specific LLMs can be like hiring employees with specialized expertise in a specific field. It’s a conscious decision rooted in the understanding that their effectiveness within the domain will surpass that of a generalist. Enter ACE LLM for your Industry.

Unveiling the Power of Industry-Specific ACE LLMs

Whether it’s utilities, banking, finance, retail, or any other vertical, industry-specific ACE LLMs are tailored to handle the nuances, jargon, and context unique to a particular sector. Drawing on proprietary and public data sources, industry documents, and synthetic data from Banking, Insurance, Retail & e-Commerce, and other industries, these fine-tuned models understand each vertical’s micro-journeys and use cases.

Check out an example of responses generated by the ACE Banking LLM:

Elevating Workflows: Wisdom from Data

Raw data, though abundant, is meaningless unless it is tailored to suit your industry, workflows and processes, and insights. Industry LLMs that are fine-tuned with relevant data sources can develop a deep understanding of industry-specific terminology, trends, and challenges. This enables more accurate and contextually appropriate interactions with users.

Take for instance onboarding as a use case. Onboarding automation can be important for both Banking and a University. While both Banking and Education Industries might share certain similarities in their abilities to automate onboarding processes, they would have specific needs and nuances of their respective industries.

While Banking LLM would be fine-tuned to reach the goal of eKYC and credit score check, the Education LLM would be fine-tuned to know more about the students’ subjects, test scores, and past certifications. Same use-case, but very different handling.

Conversations Galore: Intelligent Interactions

The rise of Industry-specific LLMs marks a new era in human-machine interactions. With their ability to comprehend industry-specific language and context, they can engage in conversations that go beyond generic responses.

This transforms discovery, purchase, and customer support, enhancing the quality of interactions and increasing customer satisfaction. This also means that the answers are customized based on a user’s behavioral pattern making the outcome far more intimately aligned with the requirements of the user. 

Industry LLMs can navigate through the specific micro-journeys of different verticals. For example, in Banking, it can assist with account inquiries, loan applications, and investment advice, while in Retail & e-Commerce, it can aid in product recommendations, order tracking, and returns processing.

Customer Experience Reinvented: Personalization Redefined

Personalization has become the cornerstone of exceptional customer experiences. AI that operates on Industry-specific ACE LLMs takes personalization to new heights by analyzing vast customer datasets to understand preferences, behaviors, and needs.

Armed with this knowledge, businesses can tailor their offerings, marketing strategies, and communication styles in ways that resonate with individual customers.

For instance, an e-commerce company can leverage Industry-specific LLMs to curate product recommendations that align with a customer’s unique tastes. In travel & hospitality, these models can craft personalized travel itineraries based on a traveler’s interests and past preferences, creating memorable experiences that foster brand loyalty.

The Path Forward: Embracing Industry-Specific ACE LLMs

As business leaders navigate the choice paradox of how to go about building and implementing their Generative-AI strategy, Industry-specific ACE LLMs, built atop a variety of Foundation Large Language Models, emerge as indispensable allies.

Beyond the generation of text or responses, industry-specific ACE LLMs play a broader role. They act as intelligent coordination layers, overseeing tasks and procedures within an organization’s specific domain. These models utilize domain-specific information and expertise to guarantee that the outcomes they produce adhere to the industry’s standards and criteria.

In essence, your organization would gain a vastly intelligent, never-tiring, unbiased employee who will remain loyal and ensure that your customers remain loyal too! 

So, let’s begin your journey with industry-specific LLMs. Time to empower your organization with the ACE of spades!

Vartika leads marketing at Gupshup and has ~16 years of experience in marketing across early-stage startups and large enterprises. A Marketing specialist by profession, a wanderer at heart, an enthusiastic cook, and a painter for the soul.

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