The prevalence of artificial intelligence across industries has introduced new and unfamiliar terms. It’s possible you’ve had a number of conversations about your organization’s AI strategy and how to utilize things like “LLMs” without ever asking: what is an LLM?
In this blog post, we will explore the concept of large language models (LLMs) and their relationship with knowledge management. We’ll highlight real-world examples for how LLMs transform your organization’s knowledge into action.
What is an LLM?
Large language models are a type of artificial intelligence (AI) infrastructure used to generate human-like text-based content based on the input they receive. In most instances an “input” is the prompt you provide a tool like Chat-GPT by typing out your requests. AI infrastructure is a complex field and the actual technological wonder of LLMs lies in understanding concepts like models, neural networks, transformers, and deep learning. For the common layperson — and for the majority of uses of LLMs — this advanced understanding of the technology isn’t necessary.
The most important thing to know about LLMs is they rely on other data to understand concepts. For example, an LLM can be fed a dataset that includes content written in English as a means to learn the rules of the English language. If this dataset is predominantly a specific type of written content, then the LLM may pick up rules based on trends in the data even if the trend isn’t intentional.
For instance, if an LLM is fed written content originating from a part of the world that doesn’t have a lot of snow — the LLM may have limited exposure to the concept of snow. This can be a challenge if you ask an LLM questions like “what’s the best clothing for cold weather?”, or “what infrastructure material is ideal for municipal roads?” Popular tools utilizing LLMs — such as Chat-GPT — use datasets that are so large it is unlikely this reductive example will occur. However, if you are integrating an LLM for your own organization, based on your own organization’s data, then this bias derived from your own content can be a challenge.
The Role of Knowledge Management in LLMs:
Knowledge management (KM) is the processing of capturing, organizing, and sharing knowledge within an organization. Successful knowledge management involves creating systems and tools to enable employees to access your organization’s knowledge quickly and efficiently. Knowledge management is a necessity for some industries (such as contact centers) but it’s usefulness is becoming more prevalent due to the increase interest in artificial intelligence.
If your own employees are having difficulty navigating your knowledge infrastructure, they may be able to troubleshoot the problem and get their work done. However, if an LLM pointed at your organization’s files can’t navigate your knowledge infrastructure then you’ll get hallucinations, inaccuracies, and potential security risks. This is because LLMs are rely on the dataset fed to them to be effective. If you feed an LLM your organization’s chaotic knowledge infrastructure then you’ll get unsatisfactory results.
In a phrase: garbage in, garbage out.
Knowledge Management and LLM Use Cases:
One of the most prevalent use cases of LLMs and knowledge management is enhancing the search experience. This is a necessity for any organization or industry that handles a tremendous amount of data. Traditionally, these searches relied on keywords — which often led to irrelevant or incomplete results. Imagine a concert venue attempting to bring up ticket sale information for an event where “The Who” headlined and the search providing every instance of the words “the” and “who” in the database. These types of problems were common for web search (such as Google) and eventually resolved, but it’s very common for internal databases to suffer the same challenges that were prevalent decades ago.
With the integration of LLMs, the search experience is enhanced. An LLM can analyze context to narrow down search results to highly relevant content and provide users with precise, well-formatted answers to queries. Let’s explore some real-world examples.
What is an LLM’s use for Pharmaceutical Research
A leading pharmaceutical company partnered with an AI solutions provider to enhance their knowledge management system. Specifically, the pharmaceutical company wanted to increase productivity by reducing the time spent identifying relevant information from research articles stored within the company’s database. The AI solution provided identified and extracted key insights from medical research articles and utilized generative AI (GenAI) to produce summaries of the content sourced for the search. This solution — which did not require any content migration or significant internal overhaul — resulted in a tenfold increase in efficiency when retrieving information from research articles.
What is an LLM’s use for Law Firms?
Several AI evangelists have suggested knowledge workers such as legal clerks may be the first to be affected by the potential value of LLMs. This is largely because the work of legal clerks is everything an LLM excels at: searching, finding, reviewing, and summarizing complex legal documents. Unlike many other industries, law firms rely on databases with complex material such as administrative filings, nuanced legal arguments, and a smattering of case-related documents (such as emails, transcripts, certificates, receipts, etc.). This type of knowledge base is the perfect use case for enhancing an organization’s productivity by making this knowledge immediately accessible with a search rather than hours of scavenging through file cabinets in the basement.
What is an LLM’s use for Customer Support?
We mentioned contact centers earlier in this article and for good reason: they’re one of the few departments that already rely on quick and accurate knowledge retrieval. Contact centers have measurable proof of the advances in productivity and efficiency by comparing their operations before and after integration AI into their operations. LLMs can be used to leverage customer data and retrieve past conversations for full context when they communicate with support. Customer support teams now have access to AI copilots that can generate responses based on the organizations documentation and customer questions, quicker then humanly possible. This can be accomplished even if a support center’s staff has high turnover or requires onboarding a lot of information for new hires.
Conclusion
Your organization’s knowledge is the bedrock for a large language model serving your organization. Your knowledge is what the LLM uses to produce accurate and reliable answers to your team’s queries. By leveraging LLM capabilities, businesses can enhance productivity, unlock knowledge stored within your organization, and provide better customer experiences. This is possible not because LLMs are magic technology that can’t fail — but because of the strength of your knowledge base. The leaders of AI innovation will ensure proper guardrails are placed to mitigate risks and ensure the accuracy and reliability of an LLM’s output.
Providing LLMs with the valuable content they need to train on your organization’s knowledge will make the difference between success and failure.
