Introduction

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Companies are investing heavily in AI solutions and technology that leverage Large Language Models (LLMs). Success of these initiatives requires accuracy, relevance and reliability of information output — something that's generally elusive to LLM-based solutions. This White Paper covers how focusing on curating, structuring and deploying institutional knowledge in the form of a Knowledge Domain delivers the accuracy, relevance and reliability needed for a successful AI/LLM solution implementation — ensuring  optimal value and minimal risk.

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“Language” vs. “Knowledge”: 
The Achilles Heel of AI/LLM Solutions

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As probabilistic language completion engines, AI/LLMs are expert in how to “speak” and “read” natural language—including style, nuance and clarity.  This provides enormous capability—not only in text completion, but also in summarization and categorization.  These are enormously valuable capabilities that are useful in specific use cases that businesses find valuable.

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Unfortunately, while AI/LLM solutions are great at knowing how to speak and read, they are not at all good or at least reliable at knowing what to say.  Unlike traditional database applications, AI/LLM accuracy of knowledge is a secondary outcome.  The vast swaths of data from across the Internet that is used to train AI/LLM solutions, while an excellent aggregation of style and nuance, presents challenges in building knowledgebases.  The diverse sources that make up the large training data set not only bring varying degrees of reliability and relevance, but also a considerable amount of inconsistency, contradiction, and gaps in knowledge.  Due to its size, there is a marked absence of data curation in the management of this training data, compromising the reliability of the knowledge in these models.

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When looking at how AI/LLM solutions are deployed, one must view how they are configured to address the “Language” vs. “Knowledge” dichotomy.  In raw form, AI/LLM solutions are “Language” tools—not “Knowledge” tools.  Sam Altman, CEO of OpenAI admits “The right way to think of the models that we create is a reasoning engine, not a fact database. They can also act as a fact database, but that's not really what's special about them – what we want them to do is something closer to the ability to reason, not to memorize.”

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Businesses typically assume all computer systems are “Knowledge” tools that deliver accurate information.  This assumption overlooks the inherent limitations imposed by probabilistic completion engines and the reliance on data quantity over quality. Consequently, businesses may overestimate the capabilities of their AI/LLMs, leading to misplaced trust and suboptimal decision-making.

The use of raw AI/LLM “Language” tools as “Knowledge” tools can have profound adverse implications for business success and risk management. Inaccuracies, inconsistencies, and gaps in AI/LLM-generated content can lead to misguided decisions, operational inefficiencies, and reputational damage. Moreover, a lack of transparency in how AI/LLMs arrive at their completions exacerbates risks, undermining trust and confidence in their outputs.

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Harnessing Institutional Knowledge

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Institutional Knowledge consists of the corporate insights, experiences, collective wisdom, know-how, and expertise unique to a business. This knowledge resides in reports, documentation, emails and most significantly in the minds of employees, forged through years of work product, successes, and failures. Institutional Knowledge serves as a guiding light, informing decision-making, problem-solving, and innovation across all levels of the organization.

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Challenges in Managing Institutional Knowledge: Despite its inherent value, institutional knowledge often remains siloed, fragmented, and inaccessible within organizations. The transient nature of employee turnover, coupled with inadequate knowledge management practices, poses significant challenges in capturing, codifying, and disseminating institutional knowledge effectively. On the flip side, the proliferation of digital tools and platforms can exacerbate information fragmentation, leading to information production overload and cognitive overload for employees.

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Knowledge Management Goldilocks Practices: To harness the full potential of institutional knowledge, businesses must prioritize effective knowledge management practices. This entails creating a culture that values knowledge sharing, collaboration, and continuous learning. Implementing robust knowledge management systems, such as centralized repositories, collaboration platforms, and mentorship programs, can facilitate the capture, organization, and dissemination of institutional knowledge. Additionally, leveraging technology, such as knowledge management tools, can augment human efforts in capturing and retrieving institutional knowledge efficiently.

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By prioritizing effective knowledge management practices and leveraging technology where appropriate, businesses can unlock the full potential of institutional knowledge, paving the way for sustainable growth, competitive advantage, and organizational excellence.

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Using Institutional Knowledge to Transform AI/LLM into “Knowledge” Tools

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Where Retrieval Augmented Generation (RAG) Fits In: RAG combines the strengths of knowledge retrieval-based systems with probabilistic completion models, enabling dynamic content generation based on retrieved knowledge. By limiting the solution’s context to institutional knowledge, RAG bases LLM outputs on real-time, organization-specific insights. Unlike the typical approaches to customize models such as training and fine-tuning, RAG is inherently change-tolerant, as the underlying knowledge sources can be updated or replaced without retraining the model. This makes RAG particularly suitable for dynamic domains that frequently change, where institutional knowledge evolves rapidly.

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The Institutional Knowledge Management Caveat: While RAG holds promise in dynamically integrating institutional knowledge with LLM outputs, its effectiveness hinges on the quality and management of institutional knowledgebase itself. Even though RAG is inherently change-tolerant, if institutional knowledge harbors gaps, inconsistencies, or contradictions, the risk of generating flawed outputs persists. Effective curation, management, and optimization of institutional knowledge are imperative for maximizing the potential of RAG and ensuring its alignment with organizational objectives.

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In sum, the strategic integration of institutional knowledge and LLMs presents businesses with unprecedented opportunities to enhance decision-making, problem-solving, and innovation. With system training, fine-tuning, and RAG approaches, businesses can harness the full potential of their organizational expertise while capitalizing on the capabilities of LLMs. By aligning the chosen approach with domain characteristics, change tolerance, and organizational objectives, businesses can optimize their operations and drive sustainable growth in an ever-evolving landscape.

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Preparing Institutional Knowledge for Use with AI/LLMs: 
The Knowledge Domain

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A Knowledge Domain is a structured collection of information, insights, and expertise relevant to a specific domain or area of knowledge within an organization. It encompasses both explicit knowledge, such as documented procedures and best practices, and tacit knowledge, including employee expertise and institutional wisdom. By consolidating and organizing this wealth of information, a Knowledge Domain provides a foundational framework for AI/LLMs to operate effectively, delivering accurate and relevant information to users.

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Building a Knowledge Domain is a multi-step process that begins with a thorough understanding of the organization's information landscape. This involves identifying and cataloging all relevant sources of data and knowledge, ranging from internal documents and databases to external research and industry reports. The next step is to structure and organize this information in a coherent manner, ensuring that it is easily navigable and accessible to users and AI/LLMs alike.

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Knowledge Domains are particularly effective in addressing one of the chief issues of AI/LLMs: output inaccuracy and hallucinations driven by gaps and contradictions in the data.  While raw AI/LLM solutions routinely experience issues due to the large and relatively un-curated nature of the training data, RAG-based AI/LLMs that incorporate Institutional Knowledge can still lead to conflicting or contradictory information, undermining the reliability of AI-driven insights and recommendations. By establishing clear guidelines for data collection, validation, and management, a Knowledge Domain helps mitigate this risk, ensuring that AI/LLMs have access to correct and consistent information.

Additionally, a well-designed Knowledge Domain employs advanced techniques such as Smart Splitting to analyze the underlying context and semantics of the content. This ensures that knowledge objects are aligned with the organization's terminology and structure, enhancing the relevance and usability of the information for AI/LLMs.

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Furthermore, a Knowledge Domain addresses the data completeness challenge by capturing supplemental knowledge that may be missing from documented sources. This includes tacit knowledge embedded in employee expertise, as well as insights gleaned from informal communications and interactions within the organization. By leveraging these additional sources of information, a Knowledge Domain enriches the underlying knowledge base, providing AI/LLMs with a more comprehensive understanding of the domain.

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In this way, a Knowledge Domain serves as a critical bridge between institutional knowledge and AI/LLMs, addressing key challenges such as data inconsistency, incompleteness, and ambiguity. By providing a structured repository of information and insights, enhanced with techniques like Smart Splitting and supplemental knowledge capture, a Knowledge Domain empowers AI/LLMs to operate more effectively, driving smarter decision-making, and enabling organizations to unlock the full potential of their data and expertise.

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The DBMT Approach to Building Optimized,
Enriched Knowledge Domains

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DBMT has revolutionized transformation of explicit and tacit institutional knowledge into meticulously curated Knowledge Domains. This transformation involves not only structuring and organizing data. but also enriching it with contextual understanding and semantic coherence, creating a superior Knowledge Domain that serves as a dynamic, trustworthy repository of knowledge, ensuring users the most accurate and relevant information.

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Step by Methodical Step: First and foremost, the DBMT team conducts a thorough knowledge discovery process to identify all relevant sources of information within your organization. This includes both documented sources, such as manuals, reports, and policies, as well as undocumented sources, such as employee expertise and institutional knowledge. By capturing this wealth of information, we lay the foundation for a Knowledge Domain that is comprehensive and extremely robust.

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DBMT then focuses on the transformation of organizational documents and data sources. This transformation involves not only structuring and organizing data, deduplication, gap identification, and contradiction resolution,. but also, through our proprietary Smart Splitting technology that analyzes the underlying context and semantics of the content to ensure that knowledge objects are aligned with the domain's terminology and structure, delivers contextual understanding and semantic coherence.

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By eliminating redundancies, identifying missing information, and resolving conflicting data, we create a Knowledge Domain that serves as ideal context for AI/LLM solutions, driving accurate and relevant responses.

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Further, we recognize that knowledge is not static; it evolves over time in response to changing organizational needs and external factors. As such, our approach emphasizes continuous learning and improvement, enabling the Knowledge Domain to evolve alongside the organization as part of a dynamic and adaptable knowledge ecosystem. DBMT provides an intuitive update and administrative interface that allows your staff to easily manage and maintain the Knowledge Domain over time. This includes the ability to replace documents or individual sources without rebuilding the entire Knowledge Domain, ensuring that it remains up-to-date and relevant as your organization evolves.

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Through ongoing monitoring, analysis, and refinement, DBMT ensures that our clients’ Knowledge Domains remain current, relevant, and aligned with organizational objectives. This commitment to continuous improvement further ensures that clients are equipped with a knowledge ecosystem that not only meets their current needs, but also anticipates future requirements and opportunities.

 

Conclusion: Knowledge Domains are a necessity when deploying AI/LLM solutions for business. With DBMT’s unparalleled expertise and innovative methodologies, we maximize accuracy, relevance, and value in our clients’ AI endeavors. Don't settle for subpar solutions—embrace the power of Enriched Knowledge Domain Development and ensure your AI solution is a winner - delivering exactly what you need.