In this post, we dive into LLMs and how they can be used decision management. This is a continuation of our series on what’s new in SMARTS™ Washington.
What are LLMs?
LLMs stand for large language models and are a type of generative AI that can “understand” human language and respond by generating new text. Generative AI is a subset of artificial intelligence that focuses on generating new content such as text, audio, images, and video. LLMs are deep learning models, a type of machine learning that uses neural networks to simulate human cognition. These models are trained on vast amounts of text and applies probabilities to predict how to logically respond. LLMs rely on transformer models, a type of deep learning that can pick up on context through a technique called self-attention. As a result, LLMs can interpret human language that is misspelled, ill-defined, colloquial, or regional and respond coherently. LLMs offer greater flexibility and fluency than traditional natural language processing systems.
What are LLM Examples?
Several prominent LLMs have emerged in recent years, each with distinctive capabilities. Here are just a few examples:
- GPT-4 by OpenAI: ChatGPT took the world by storm since it was first released in 2022. GPT-4 is the latest iteration which allows for both language and image processing and generation. GPT-4 is known for its versatility across various domains.
- Gemini by Google: Similar to GPT-4, Gemini is multi-modal, handing text and images as well as audio and video. Gemini is integrated into many Google applications and products and comes in various sizes to balance capability with efficiency.
- Llama by Meta: Llama is an open-source family of models trained on a variety of public data sources, including GitHub and Project Gutenberg. It has become popular among researchers and developers and have been adapted for various specialized applications.
- Claude by Anthropic: Claude focuses on constitutional AI, which means AI outputs are guided by a set of principles. As a result, Claude is known for thoughtful, nuanced responses and best used for complex tasks where context matters.
- DeepSeek-R1 by DeepSeek: DeepSeek showed the world that Silicon Valley doesn’t have the monopoly on AI innovation when it released R1 earlier this year. R1 is an open-source model that uses reinforcement learning techniques to refine its ability to critically and creatively problem solve. The model applies chain-of-thought reasoning before giving a response.
What are LLM Use Cases?
LLMs have already transformed numerous departments and industries through their versatile applications. Examples include:
- Content Creation in Marketing: Marketers are leveraging LLMs to draft articles, blog posts, headlines, and other marketing copy. While the content that LLMs create typically aren’t fit for publication as-is, LLMs can accelerate the overall production process.
- Smarter Chatbots in Customer Service: Early versions of chatbots relied on humans anticipating customer questions and coming up with rigid processes and pre-canned responses. Through LLMs, businesses simply need to provide the business context and guardrails. Chatbots will generate the appropriate responses, providing an overall better customer experience.
- Software Development in Software Engineering: Developers are leveraging LLMs for explaining legacy code, generating new code, debugging, documenting, and more.
- Diagnostics in Healthcare: Doctors are leveraging LLMs to summarize relevant patient data and generate preliminary diagnostics.
- Legal Research in Law: Paralegals and lawyers are leveraging LLMs to summarize documents, analyze cases, and identify relevant precedents.
LLMs in Decision Management
In decision management, LLMs can be used to streamline, support, and augment decision-making processes. Here are a few ways:
- Providing Context Awareness: Decision management projects can be very complex, comprising of various models, decision trees, and business rules compiled by various groups over multiple years. By summarizing documentation and files, LLMs can provide the context that newly assigned business analysts need before embarking on a new project.
- Personalizing Decision Development: Most decision management systems have their own language and processes that users have to adapt to in order to develop decisions. LLMs enable users to use their own language and follow their own processes. LLMs will create the appropriate assets that can be understood and executed by the decision management system.
- Scenario Analysis: LLMs can rapidly generate and evaluate multiple decision scenarios to help teams understand potential outcomes in different condition. In addition, LLMs can identify bias and other risks that are influencing decision outcomes.
The benefits of LLM are why we at Sparkling Logic launched LLM-powered AI Assistant earlier last year. AI Assistant is an interactive virtual assistant that supports users through out the decision management lifecycle. Users can leverage AI Assistant to get answers to product-related questions, explain existing decision logic, create/modify project assets such as data models, test data, and various forms of decision logic, including dynamic questionnaires, and create/run tests and simulations. Eventually, users will be able to leverage AI Assistant in all aspects of decision management.
The Future of LLMs in Decision Management
As LLMs continue to evolve, we can expect the following improvements in decision management:
- More sophisticated domain specialization
- Improved integration with other AI systems such as predictive analytics and computer vision
- Enhanced explainability to make LLM reasoning more transparent
- Better security in terms of handling proprietary information
In other words, over time, we can expect virtual assistants to mature into trusted advisors. The most successful organizations will be those that thoughtfully integrate LLMs into their decision-making processes.
Learn more about AI Assistant, Sparkling Logic’s interactive tool powered by LLM.