Generative AI Landscape

Generative AI Landscape: Exploring the Current Landscape and Exciting Use Cases

The field of Generative AI has rapidly evolved over the years, with various models and applications emerging to revolutionize the way businesses operate. In this blog post, we will explore the current landscape of Generative AI, including popular models such as Diffusion Models, Large Language Models (LLMs), and ultra-specialized generative models for industries like drug discovery. We will also delve into the most valuable use cases and applications observed so far, covering areas such as customer support, augmented coding, and ideation.

Current Landscape of Generative AI

Diffusion Models

Diffusion models have gained significant popularity in creative spaces, such as music, video, and picture generation. However, their adoption in business settings remains limited. These models are primarily used for image and video generation, but their application in other domains is still under exploration.

Large Language Models (LLMs)

LLMs represent the majority of use cases in the business world, particularly in chatbot-style interactions. They are widely used for tasks such as answering questions, generating text, and processing natural language queries. The versatility of LLMs makes them suitable for a broad range of applications within organizations.

Specialty Models

Ultra-specialized generative models have shown immense value in specific problem domains, such as drug discovery. These models are designed to address unique challenges within industries and have proven to be highly valuable when applied to their intended use cases.

Types of Chatbots

Chatbots are a prevalent use case for LLMs in businesses, and they come in three major varieties:

  1. Knowledge Domain Chatbots: These chatbots are designed to authoritatively answer questions about a domain of knowledge. They utilize vectorized chunks of text for semantic retrieval and often incorporate hybrid search techniques. Knowledge Domain chatbots typically work with unstructured data, such as documents, policies, procedures, blog posts, and news articles.

  2. Natural Language Query (NLQ) Chatbots: NLQ chatbots leverage LLMs along with examples of a database schema to generate queries against structured data stores like databases. They then provide natural responses using the resulting data set.

  3. Tool Use Bots: These chatbots are similar to NLQ bots but are designed to work with REST APIs. The LLM is provided with a set of “tools” or APIs it can call to accomplish tasks or answer questions. It typically determines the parameters for the API, which is then called, and the LLM generates an answer using the returned values from the API.

Agentic Chatbots

Agentic chatbots combine the reasoning capabilities of LLMs with a set of tools, knowledge domain, or natural language query systems to service complex, multi-step requests. While their reliability is still improving, agentic approaches will become increasingly useful as underlying LLM models advance in complexity.

Exciting Use Cases for Generative AI

Generative AI has numerous applications in various domains, with some of the most valuable use cases including:

  1. Intelligence Augmented Workforce: Vectorizing all of a company’s knowledge and utilizing chatbots to work alongside employees can significantly accelerate productivity across the organization. This approach allows employees to focus on higher-level tasks while the chatbot handles routine inquiries.

  2. Personal Brains/External Brains: Transcribing and summarizing calls, along with building a vectorized database of customer interactions, can provide nearly perfect recall for salespeople. This capability enables them to access critical information quickly and efficiently.

  3. Customer Support Deferral and Augmentation: Chatbots can handle a significant portion of customer support inquiries, freeing up support teams to tackle more complex issues. This approach not only improves the customer experience but can also lead to cost savings for the organization.

  4. Augmented Coding Co-Pilots: Chatbots with access to an organization’s entire codebase can assist development teams with code consistency and development velocity. This collaboration can help ensure that code adheres to established standards and accelerates the development process.

  5. Ideation and Sounding Board Chatbots: These chatbots, equipped with critical business metrics, can provide valuable input on key decision-making processes. By taking emotion out of the equation, these chatbots can help organizations make more informed and objective choices.

In conclusion, the landscape of Generative AI is rapidly evolving, with new models and applications emerging continuously. By understanding the current landscape and exploring exciting use cases, organizations can harness the power of Generative AI to drive innovation and improve their operations. As the underlying technology continues to advance, the possibilities for applying Generative AI in businesses will only grow, making it a critical component of modern organizations.

  • Human Intervention: None

Facts Used:

    • Current landscape for GenAI comes down to Diffusion Models, Large Language Models (LLMs) and ultra-specialized generative models for industries like drug discovery
    • Diffusion models are popular in the creative spaces (music, video, picture generation) but rare in business settings.
    • Specialty models are highly valuable but only for specific problems
    • Large language models represent the majority of use cases in business and most of these are Chatbot style use cases.
    • Chatbot is a very broad term which includes user to chatbot interactions or even chatbot to chatbot or machine process to chatbot.
    • Chatbots come in 3 major varieties: Knowledge Domain (traditional RAG), Natural Language Query (NLQ), and Agentic/Tool Use bots
    • Knowledge Domain chatbots are designed to authoratatively answer questions about a domain of knowledge and are made up of vectorized chunks of text for semantic retrieval (and very often lexical with hybrid search). Typically unstructured data like documents, policies and procedures, blog posts and news posts.
    • Natural Language Query is using the LLM, along with examples of your database schema, to generate queries against structure data stores like databases and then provide natural responses using the result set.
    • Tool use bots are similar to natural language query, except they are meant to be used against REST APIs. The LLM is give a set of “tools” or APIs it can call to accomplish tasks or answer questions. It typically just figures out parameters for the API. The REST endpoint is called and the LLM produces an answer with the resulting returned values from the API.
    • Agentic bots use the reasoning capabilities of the LLM along with a set of tools, knowledge domain or natural language query systems to service complex mutli-step requests.
    • Different routing methods are emerging to coordinate all these emerging chatbot implementations. Some organizations use Agents with a list of bots to figure out which bot can do what task. Sometimes they will be @ coded, and called like a slack or teams user. Others have developed semantic lookup with vector search to route to specific bots.
    • To this day, knowledge domain chatbots that represent a document or set of documents are still the most popular chatbots that organizations are producing, while NLQ is starting to become more popular. The reliability of agentic approaches is still quite low, so it’s not as popular.
    • Agentic approaches will become more useful as the underlying LLM models advance in complexity
    • Intelligence Augmented Workforce, the idea of vectorizing all of your company knowledge and having a chatbot work alongside every user to accelerate everyone in the org is one of the most valuable use cases observed so far.
    • Personal Brains/External Brains are not as popular, but can be game changing for the individual. Transcribing and summarizing calls and building up a vectorized database of all your customer interactions gives you nearly perfect recall. The perfect salesman.
    • Customer support deferral and augmentation are some of the most financially exciting use cases. We blogged about this previously
    • Augmented coding co-pilots that have access to the entire organization codebase are extremely valuable to development teams for code consistency and development veolocity
    • Ideation and sounding board chatbots that have critical business metrics augmented into them can help take the emotion out of critical business decisions. This is an exciting, emerging use case.
    • Generative AI can help your organization in many ways, these are just a few that I talk about daily with our customers.