Grounding LLMs: Your Competitive Advantage in the GenAI Revolution

Generative AI (GenAI), and more specifically, large language models (LLMs), are revolutionizing business processes across various industries, promising significant advancements in efficiency, innovation, and customer engagement. However, understanding GenAI’s capabilities and limitations is essential for leveraging its full potential while avoiding common pitfalls.

The Allure and Risks of GenAI

GenAI offers transformative potential, enabling businesses to automate tasks, generate insights, and create personalized experiences at scale. Successful use cases include automated content creation, personalized marketing, and predictive maintenance. These applications showcase how GenAI can enhance productivity and innovation.

However, the rush to implement AI without fully understanding its limitations has led to numerous project failures. According to the Harvard Business Review, 80% of AI projects fail, double the rate of corporate IT project failures from 10 years ago. A primary reason for these failures is the misconception that GenAI can handle decision-making tasks, which it inherently struggles with due to its lack of contextual understanding and inability to perform quantitative analysis effectively.

Here are just two examples:

• Air Canada was ordered to pay customer damages after its chatbot provided incorrect information about a bereavement discount.
• In the Netherlands, an automated system incorrectly flagged thousands of families as making fraudulent benefit claims due to bias in the training data.

As LLMs become more widely used, more examples of failures are reported. There is even an archive on GitHub dedicated to recording “LLM Failures” from both Bing and ChatGPT. Examples include hallucinating non-existent citations for a law case and failing to answer simple maths questions on prime numbers.

Avoid Failures – Understand the Limitations of LLMs

GenAI excels at generating predictive text based on training data but cannot make informed business decisions. Recent studies have shown that LLMs struggle with data analysis, quantitative reasoning, and causal reasoning. LLMs operate without understanding context or consequences, making them unsuitable for critical decision-making tasks.

LLMs have a number of limitations that need to be appreciated:

• Causal Reasoning Deficiency
  LLMs excel at recognizing patterns and correlations in unstructured text but struggle with causal reasoning. They can’t reliably identify cause-and-effect relationships, crucial for making informed business decisions. For example, LLMs struggle to predict the impact of market changes on sales or to identify the root cause of operational issues.
• Handling Structured Data
  While LLMs can process natural language text effectively, they are not designed to work with structured data such as databases and spreadsheets. This limitation restricts their utility in data-driven environments where structured data analysis is essential for operational efficiency and strategic planning​.

• Quantitative Analysis
  Enterprises rely heavily on quantitative analysis for decision-making. Tasks such as financial forecasting, statistical modeling, and performance metrics evaluation demand precise calculations and a solid grasp of mathematical principles. LLMs are not inherently equipped for these tasks and can struggle with even basic mathematics.

• Decision-Making Support
  Effective decision-making in enterprises often depends on insights derived from qualitative and quantitative data. LLMs can assist with generating reports and summarizing information but fall short in providing deep analytical insights necessary for strategic decisions based on quantitative data​.

• Integration Challenges
  Despite their capabilities in natural language processing, integrating LLMs into workflows that involve unstructured and structured data presents challenges.

In addition to these reasoning challenges, LLMs also have other areas to consider:

• Data and Bias Issues – The quality of LLM outputs is heavily influenced by the training data, which can contain biases. These biases can lead to flawed outcomes, as seen in the above case studies.
• Hallucination – While hallucination can be beneficial in creative contexts, it is detrimental in decision-making (see example of hallucinated law cases above). LLMs can generate plausible but incorrect information, leading to unreliable conclusions.
• Explainability—LLMs lack explicit models to explain their recommendations, making it difficult to understand the reasoning behind their outputs. This lack of transparency poses challenges in validating and trusting AI-driven decisions.

Overcoming LLM Limitations – Introducing Grounding

So, how can you overcome the challenges of using LLMs in the enterprise? One approach is to use additional data, or models, to “ground” the LLMs in the real world.

Grounding offers a pathway to unlock LLMs’ potential while mitigating the risks associated with their ungrounded use. In the context of an LLM, grounding refers to the process of linking AI outputs to real-world contexts and knowledge. It involves integrating LLMs with external data sources, continuous learning mechanisms, and domain-specific insights to ensure that their capabilities are grounded in reality and aligned with the specific needs of the business environment.

For large businesses, this means integrating AI capabilities with domain-specific data and continuously updating the AI’s knowledge base. By incorporating contextual relevance, data integration, continuous learning, and transparency, businesses can mitigate the risks associated with LLMs and leverage their full potential.

Grounding offers numerous benefits, including increased accuracy, reliability, and trust in AI applications. Grounding involves incorporating specific, relevant information tailored to the use-case that is not inherently part of the AI’s training data. This could include industry-specific knowledge, proprietary data, or real-time information enhancing AI’s ability to provide accurate and actionable outputs.

These are some of the key considerations when grounding LLMs:

• Data Integration:
  Combining LLM capabilities with external data sources or real-time data is a key aspect of grounding. By integrating structured data from databases, spreadsheets, or IoT devices, LLMs can leverage quantitative information and perform tasks like data analysis, forecasting, and decision support.
• Continuous Learning:
  Grounding also involves updating the AI’s knowledge base regularly to reflect new information and evolving contexts. This ensures that the AI’s outputs remain relevant and up-to-date, preventing it from relying on outdated or stale information.
• Transparency and Interpretability:
  Ensuring that LLM outputs are understandable and that the processes behind them are transparent is crucial for grounding. This interpretability makes it easier to validate and trust the AI’s decisions, particularly in critical business scenarios.

Strategies for Grounding LLMs

There are several strategies for grounding LLMs – we consider three common ones.

Retrieval Augmented Generation (RAG):

One approach to grounding LLMs is Retrieval Augmented Generation (RAG). In this method, the model dynamically retrieves relevant information from a database or document collection to augment its response generation process, thus incorporating use-case-specific information that isn’t part of the LLM’s pre-trained knowledge. However, this approach has limitations, including the need for access to usable data and the reliability of that data.

Fine-tuning:

Fine-tuning involves providing additional training to infuse the model with task-relevant data to improve performance. However, it is an expensive process, and improvements can be slight in many cases.

Causal Reasoning:

Deploying a causal model allows LLMs to interact and answer questions (interventions and counterfactuals) translated from natural language. This approach enables an LLM to reason about cause-and-effect relationships, a critical capability for decision-making in business environments.

For example, a causal model could be used to ground a LLMsystem in a manufacturing context, allowing it to reason about the causal relationships between different factors (e.g. raw material quality, production processes, and equipment maintenance) and their impact on product quality or throughput. This grounding would enable the AI to provide reliable recommendations for optimizing operations or troubleshooting issues.

Challenges in Grounding LLMs

Grounding LLMs solves many issues of using an LLM in business decision-making scenarios. Of course, it is never that easy, and there are challenges in implementing grounding successfully.

Integrating grounding principles into existing AI systems presents technological, operational, and conceptual barriers. Combining LLMs with external data sources, causal models, and continuous learning mechanisms can be a complex undertaking, requiring specialized expertise and careful system architecture.

At causaLens, we advocate a platform approach to support these complex integrations. Data science platforms like decisionOS can provide a unified environment for integrating your chosen LLM with grounding components, streamlining the process and enabling efficient management and deployment of grounded AI systems.

We also appreciate the value domain experts can add to the grounding process. Collaboration between AI developers, domain experts, and scientists is crucial for infusing GenAI projects with a broad spectrum of knowledge and practical insights. This interdisciplinary approach ensures that the AI systems are grounded in technical expertise and domain-specific understanding, resulting in reliable and trustworthy solutions.

Human insight and intervention are necessary for creating reliable systems. Within decisionOS, we provide human-guided causal discovery, offering the most advanced human-machine engagement over an AI model. This process ensures we incorporate human expertise to validate the AI’s outputs, enhancing the system’s overall trustworthiness.

In conclusion

We have discussed the importance of grounding LLMs in real-world contexts, data, and knowledge to address their limitations and enable reliable decision-making in business environments. We explored the principles of grounding, strategies for implementing it, and the challenges and interdisciplinary collaboration required for successful integration.

In our next blog, we will explore how generative AI and causal AI solutions can be combined with an agentic AI framework. In addition to providing LLM grounding, we will look at other combined use cases that enable enterprises to explore the value of AI in complex business environments fully.

You can read more here or why not download our whitepaper on combining LLMs and Causal AI?