Creating an Enterprise Knowledge Search using Large Language Models (LLMs)

In today’s environment, enterprises, federal agencies, and departments face challenges in managing vast amounts of internal data and information. Traditional keyword searches or navigation through folder systems are no longer efficient in meeting the demands of modern-day information retrieval. A superior, more robust search system provides advantages such as providing visibility into the most relevant up-to-date, and accurate information, improved contextual information, facilitating knowledge-sharing and collaboration among employees, and helping identify knowledge gaps and areas for improvement. Implementing a system that provides better visibility into data will improve efficiency and performance when managing information and knowledge.

With the introduction of GPT and the recent popularity of ChatGPT, many are wondering if this could be the end to all search and knowledge extraction from a large corpus of enterprise data. Although it is too early to tell, we at Abeyon have explored the possibilities of this technology and how it can be leveraged for internal data search. This is our informed and researched first take on GPT.

With enterprise data, implementing a hybrid of the following approaches is optimal in building a robust search using large language models (like GPT created by OpenAI):

  • vectorization with large language models (LLMs),
  • fine-tuning of large language models, and
  • semantic search.

Vectorization of Enterprise Data: As a first step in building an enterprise data repository, enterprise data should be vectorized to create a vector repository. Documents must be preprocessed before vectorization in order to comply with the size limits of the LLMs. The vectorized data will be stored in a vector database (e.g., or

Fine Tuning Large Language Model: LLMs can be fine-tuned to understand domain-specific data. During fine-tuning, the model is trained on the dataset by providing domain-specific questions and corresponding answers, which allows it to learn how to generate appropriate answers for new questions. Once the model is fine-tuned, it can be used to generate answers for new questions by feeding in the question as input and allowing the model to trigger a corresponding answer. This process can be repeated for multiple questions, allowing the model to build a knowledge base of question-and-answer pairs. However, fine-tuning has several pitfalls which we will discuss later in this post.

Semantic Search: Semantic search, also known as neural search or vector search, uses a semantic embedding of numbers to represent the context or meaning of a   text, unlike traditional keyword searches. Semantic searches attempt to generate the most accurate results possible by understanding the search based on the searcher’s intent, query context, and the relationship between words. This allows new databases to scale and search based on the actual content and context of the records.

Search Approach: Combining all of these (vectorization, fine-tuning, and a semantic search) into a search approach will create a more robust search solution. The high-level process involves vectorizing and indexing an enterprise corpus of data with semantic embeddings, using a large language model (LLM) to generate relevant search terms or queries, and using a semantic search engine to find the most relevant documents based on those queries. Once the relevant documents are identified, the LLM can be used to quickly read and summarize the relevant parts of those documents. Finally, relevant information can be compiled together to answer the question.

Why this Approach: Generally, the advantage of using a semantic search over fine-tuning is that it can be more efficient and effective in identifying relevant documents, especially when dealing with complex and nuanced data. However, fine-tuning in some instances may be more accurate in generating answers to specific questions, especially when dealing with highly specialized domains or topics. Some domains may use specific abbreviations or terms that have a different meaning than within a general context. Fine-tuning a large language model (LLM) on a specific domain can help it understand these specialized terms and improve its accuracy in generating answers related to that domain. For example, in a set of documents related to the marine engineering industry, the term “ME” may stand for “Main Engine” rather than the usual personal pronoun “me.” If we do not fine-tune the LLM on this specialized domain, it may generate inaccurate responses by misinterpreting the meaning of ME as this personal pronoun because of its typical usage. By fine-tuning the model on this specific domain, and training it to understand the specialized meaning of “ME,” we can improve its accuracy in generating responses related to that industry.

Potential Barriers and Advantages: It is worth noting that fine-tuning a language model for specialized domains or topics can result in a loss of generalization ability, meaning that the model may not perform as well on general language tasks outside of its specific domain or topic. Nonetheless, fine-tuning remains an effective approach for improving the accuracy of language models in specialized domains or topics where specific language patterns and meanings are used.

One major issue for fine-tuning is it does not rule out confabulation or hallucination. Fine-tuning models also lack a theory of knowledge or epistemology. They cannot explain what they know or why they know it, and therefore are unreliable as a source of information. Most artificial intelligence (AI) research focuses on developing larger and more powerful models rather than investing in cognitive architecture or neuroscience. Creating a single model that understands what it does and does not know is fairly complex as AI technology stands today.

Fine-tuning a large language model (LLM) like GPT-3 can be a complex, resource-intensive and expensive process, especially when dealing with specialized domains or tasks. This is due to the numerous parameters in the model, which can make fine-tuning expensive in terms of computational resources.

In addition to the cost, fine-tuning large language models can be time-consuming. Validating the accuracy of the fine-tuned model can require a significant amount of time and effort, and may involve a trial-and-error process of adjusting hyperparameters and other configuration.

When fine-tuning language models, adding new documents to a knowledge base that has already been fine-tuned requires re-training the entire model. This can be a lengthy and resource-intensive process, especially when dealing with large datasets.

In contrast, with semantic search, the addition of new documents to a knowledge base is typically a more efficient process that does not require re-training the entire model. Instead, the semantic embeddings of the new documents can be added directly to the existing database, which can then be searched using semantic similarity metrics.

Our Recommendation: Fine-tuning large language models (LLMs) and semantic search have advantages and pitfalls. Creating a hybrid solution that leverages the benefits of these technologies and customizing the solutions with contextual data and use cases will yield results that are worth considering.


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Intelligent Process Automation

Intelligent Process Automation – Why is it important?

Intelligent Process Automation is the combination of different technologies to automate more complete, end-to-end business processes

Government agencies and enterprises have been performing business process improvements for the past several years to improve efficiency within their internal processes, reduce waste in internal workflows and streamline business functions. This has led to human workers performing tasks more efficiently. However, this consequently has led to humans performing high volumes of repetitive, mundane tasks leading to burn-out and increasing risks for human error.

Today, advances in technology have enabled the automation of human tasks, especially those that are repetitive and mundane, as well as those that involve a certain level of cognitive functionality (i.e., “decision-making”). This has enabled government agencies and enterprises to enter the next level of creating efficiency by automating functions using methods ranging from simple workflow automation to complex intelligent process automation (IPA). For instance, IPA holds the promising potential to expand automation capabilities to include additional and more complex workflows and functions that utilize both structured and unstructured data. IPA combines Cognitive/Artificial Intelligence (AI) and Robotic Process Automation (RPA). The AI provides the intelligence (i.e., the “brains”) of the process while RPA provides the processing (hands) of functional workflows. RPA, which is fundamentally rule-based with limited capabilities, enables IPA to yield high returns and better business outcomes for processes that involve well-defined rules, are repetitive, require access to multiple systems, have manual steps following Standard Operating Procedures, and have a high possibility for human error.

Intelligent Process Automation

Image Reference: – October 30, 2019

However, currently, most government agencies and enterprises are solely just implementing RPA- based automation in an attempt to improve workflows and functions that typically require less cognitive decision-making steps, but are only witnessing moderate success. The low-hanging fruit for intelligent automation is data-intensive and repetitive tasks that machines can do better and faster than humans. Many applications will keep a “human in the loop”—at least until the system has proven its reliability. Even then, AI systems by their nature learn (both in supervised and unsupervised ways) and therefore change, so they need to be reviewed on an ongoing basis to ensure they are still performing as intended.

Due to these limitations, this has led to more adoption of IPA-based solutions across agencies and enterprises which has dramatically improved organizational efficiency, reduce costs, and increase customer satisfaction levels. AI-enhanced automation can significantly expand the scope of process automation to new and exciting areas that were previously considered too complex for consideration. For instance, a primary objective of IPA is to provide the human workforce with additional knowledge, support, and insights by automating repetitive, manually-intensive, and otherwise mundane tasks. With IPA, organizations can amplify human potential and move employees from low-value work to high-value work. As an example, Abeyon is employing IPA to “read” and analyze voluminous quantities of unstructured documents to extract datasets and utilize them for further analysis.

For agencies and enterprises to adopt IPA into their businesses, a well thought-out long-term plan is needed since the ROI on IPA work will take longer than simple automation. Since AI models will need large sets of datasets to be trained on, a large initial investment will be needed to see long-term value. This involves creating a very clear picture of the cost and benefits of IPA efforts. Abeyon has worked with several government agencies and enterprises to realize the power of AI and how IPA can greatly increase value of automation.

Contact us at [email protected] if you are interested in learning more. Want to learn more about AI concepts? Click here to see our Insights series


Bring clarity to unstructured data using Natural Language Processing (NLP) – Part 2

Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language, in particular how to program computers to process and analyze large amounts of natural language data.

This is series 2 of the introduction to key capabilities of NLP technologies. Here is the link to Series 1 of this article. With recent advances in Artificial intelligence technologies, computers have become very adept at reading, understanding, and interpreting human language. Here a few additional NLP capabilities that have made that possible.

Text Clustering: 
Clustering in general refers to the grouping of similar data together. Text clustering is a technique used to group text or documents based on similarities in content. It can be used to group similar documents (such as news articles, tweets, and social media posts), analyze them, and discover important but hidden subjects. Text classification as we discussed before also puts objects in a group, but the major difference between clustering and classification is that classification is a supervised method whereas clustering in an unsupervised method. All objects/data within clustering are new and no the resultant groups are unknown. This method is heavily used to identify key topics, patterns in large data sets as the first step to classification.

Text Clustering NLP

Text Summarization: 
Text Summarization refers to the technique of producing a concise summary of long pieces of text while preserving key information, content and overall meaning. There are many reasons and uses for a summary of a larger document. One example that might come readily to mind is to create a concise summary of a long news article, but there are many more cases of text summaries that we may come across every day. There are two different approaches that are used for text summarization: Extractive Summarization, Abstractive Summarization. Extractive Summarization identifies the important sentences or phrases from the original text and extracts only those from the text. Abstractive Summarization generates new sentences from the original text.

Extractive Text Summarization

Abstractive Text Summarization



Relation Extraction:
Relationship extraction refers to the technique of extracting semantic relationships from a text. Relationship Extraction products attributes and relations for entities in a sentence. For example: given the sentence “John was born in Fairfax, Virginia” a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.

These techniques combined with techniques discussed in series 1, can provide tools to create value from the deluge of unstructured data found within government agencies and other organizations. There is much to be learned from the potential of AI and, in particular, its ability to analyze masses of unstructured data

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measure an AI models performance using F1 score

How to measure an AI models performance – F1 score explained

Organizations often ask us, “How well is the AI model is doing?” Or “How do I measure its performance?”, we often respond with “Performance of the AI model is based on what the F1 score of the model is” and we will get a puzzled look on everyones face or asking “what is an F1 score?”  So here I am going to attempt to explain F1 score in an easily understandable way:

Definition of F1 score:

F1 score (also F-score or F-measure) is a measure of a test’s accuracy. It considers both the precision (p) and the recall (r) of the test to compute the score (as per wikipedia)

Accuracy is how most people tend to think about it when it comes to measuring performance (Ex: How accurate is the model predicting etc.?). But accuracy is not a true measure of AI models performance. Accuracy only measures the number of correctly predicted values among the total predicted value. Although it is a good measure of performance it is not complete and does not work when the cost of false negatives is high. Ex: Lets assume we are using an AI model to predict cancer cells, after training, the model is fed with 100 samples that have cancer and the model identifies 90 samples as having cancer. That 90% accuracy, which sounds pretty high. But the cost of not identifying 10 samples is very costly. Therefore accuracy is not always the best measure.

So to explain it further lets consider this table:



True Positive:

True Positive is an outcome where the model correctly predicts the positive class. Ex: when cancer is present and the model predicts cancer.

False Positive is an outcome where the model incorrectly predicts the positive class. Ex: when cancer is not present and the model predicts cancer.

False Negative is an outcome where the model incorrectly predicts the negative class. Ex: when cancer is present and the model predicts no cancer.

True Negative is an outcome where the model correctly predicts the negative class. Ex: when cancer is not present and the model predicts no cancer.

As explained by the definition, the F1 score is a combination of Precision and Recall.

Precision is the number of True Positives divided by the number of True Positives and False Positives. Precision can be thought of as a measure of exactness. Therefore, low precision will indicate a large number of False Positives.

Recall is the number of True Positives divided by the number of True Positives and the number of False Negatives. Recall can be thought of as a measure of completeness. Therefore, low recall indicates a large number of False Negatives.

Now, F1 score is the harmonic mean of Precision and Recall and gives a much better measure of the model.

F1 Score = 2*((precision*recall)/(precision+recall)).

A good F1 score means that you have low false positives and low false negatives. Accuracy is used when the True Positives and True negatives are more important while F1-score is used when the False Negatives and False Positives are crucial

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Named Entity Recognition

Bring clarity to unstructured data using Natural Language Processing (NLP) – Part 1

Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language, in particular how to program computers to process and analyze large amounts of natural language data.

In my previous articles, I have addressed some specific topics on NLP like Text Classification, Natural Language Search, etc. Here I want to give a quick introduction to a few key technical capabilities of Natural Language Processing.With recent advances in Artificial intelligence technologies, computers have become very adept at reading, understanding and interpreting human language. Let’s look a few key capabilities of NLP. These are by no means a comprehensive list of all NLP capabilities.


Named Entity Recognition (NER):
NER is one of the first steps towards information extraction from large unstructured data. NER seeks to locate and extract named entities that are present in a text into pre-defined categories like persons, countries, organizations etc. This helps with answering many questions such as:
– How many mentions of an organization is in this article?
– Were there any specific products mentioned in a customer review?

This technology will enable organizations to extract individual entities from documents, social media, knowledge base etc. The better defined and trained the ontologies are, the more efficient the outcome will be.


Topic Modeling:
Topic Modeling is a type of statistical modeling for discovering abstract topics from a large document set. It is frequently used to discover hidden semantic structures in a textual body. It is different from traditional classification in that, it is an unsupervised method of extract main topics. This technique is used in the initial exploring phase to find what the common topics are in the data. Once you discover the topics, you can use language in those topics to create categories. One of the popular methods used for Topic Modeling is Latent Dirichlet Allocation (LDA). LDA builds a topic per document model and words per topic model, modeled as Dirichlet distributions. You can read more about LDA here:


Text Classification:
Text classification (a.k.a text categorization or text tagging) is the task of assigning a set of predefined categories to free-text. This is a supervised training methodology as opposed to Topic Modeling above. I have written in detail about text classification here:


Information Extraction:
Information Extraction is used to automatically find meaningful information in unstructured text. Information extraction (IE) distills structured data or knowledge from the unstructured text by identifying references to named entities as well as stated relationships between such entities. IE systems can be used to directly extricate abstract knowledge from a text corpus or to extract concrete data from a set of documents which can then be further analyzed with traditional data-mining techniques to discover more general patterns.


Sentiment Analysis:
Sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. Sentiment analysis decodes the meaning behind human language, allowing organizations to analyze and interpret comments on social media platforms, documents, news articles, websites, and other venues for public comment.


Within government agencies and organizations, there is a deluge of unstructured data both in analog and digital form. NLP can provide the needed tools to move the needle forward in providing better visibility and knowledge into unstructured data. NLP can be utilized in many ways. To name a few: Analyze public data like Social Media, reviews, comments, etc., Get visibility into the organizational knowledge base, provide predictive capabilities, enhance citizen services, etc. There is much to be learned from the potential of AI and, in particular, its ability to analyze masses of unstructured data. It is time now for agencies and organizations to take action to harness the power of NLP to stay ahead.