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Large Language Models (LLM) - What are they and how can you use them in your business?



A clock maker fine-tuning and using Rag to customize LLMs

Large Language Models (LLMs) have become popular these days for their ability to transform aspects of our business. Given where we are along the hype-cycle of AI, and more specifically Generative AI, it’s believed by some that businesses will be completely transformed by technology, or rather tools, like LLMs. But, that is not the case. However, they are a revolutionary tool that, if aligned with your business objectives, could have incredible effects on your business. In this article, we will define what an LLM is, provide some examples, discuss two of the three ways businesses can amend existing LLMs for their businesses and provide ten high-level business use cases that may be of interest to you and your business.


What is a Large Language Model (LLM)?


LLMs are a type of artificial intelligence that can understand and generate human language. These models are trained on massive amounts of text data, allowing them to identify patterns and relationships in language. This enables them to perform a variety of tasks like Generating different creative text formats and content (ex. marketing copy, emails) and Translating languages.


However, LLMs don't truly understand the meaning behind the words they use. They are incredibly good at mimicking human language patterns, but they don't have the ability to reason or form their own thoughts. This can lead to a problem we call hallucination, or in laymen terms, lying without knowing.


Using popular LLMs with human oversight


You may have heard of some language models (LLMs) like ChatGPT and Gemini. These LLMs were employed to assist in writing this article, which would usually take a day or two to research and write, but instead took a little less than three hours. LLMs are incredibly powerful, and when used in combination with human oversight from someone with a vast knowledge and understanding of a subject, such as Pipemind on AI and LLMs, hallucination and bias can be greatly reduced. Thus, this article is not just a copy and paste from an LLM. Rather, it was created using expert knowledge of the subject and then rewritten using LLM outputs and human oversight to produce a accurate knowledge dense piece of text providing insights to business professionals like yourself.

But, that’s just the tip of iceberg, what if you businesses create or amend their own LLMs specifically using their own proprietary data to improve their businesses?


Tailoring LLMs for your business


Most companies solutions should not building their own LLM from scratch due to the large financial requirements and time it takes to build them. Rather, two more economical, faster and easier approaches, which can be used in conjunction with one another are Fine-tuning & RAG (Retrieval-Augmented Generation). Fine-tuning and RAG are both techniques for improving the performance of Large Language Models (LLMs) for specific tasks, but they take different approaches. (Table 1: Summary of Fine-Tuning & RAG)


Fine-tuning is ideal when you need a highly specialized LLM for a single, well-defined task and have the resources to create and maintain a custom dataset. It is a process that involves modifying the LLM internally. This is achieved by training the LLM on a new dataset that is specifically chosen for the intended task. As a result, the LLM adjusts its internal parameters to become more accurate and effective in performing that particular task.


Fine-tuning has its strengths and weaknesses. On the one hand, it can result in highly specialized LLMs that perform exceptionally well in a specific domain. It can also improve the LLM's ability to handle complex tasks within that domain. On the other hand, fine-tuning requires significant computational resources and specialized expertise to implement. The fine-tuned LLM becomes less versatile and may not perform well on tasks outside its specialized domain. Moreover, changes made during fine-tuning are permanent, which means that the LLM needs to be retrained for adjustments.


RAG is preferable when you need a more flexible solution that leverages existing knowledge and can be adapted to various tasks within a domain. It is an approach that provides additional information to the Language Model (LLM) from external knowledge sources such as databases, or documents, based on the user's query or task. This approach retrieves relevant information and feeds it to the LLM to inform its response generation.


Strengths of RAG include its flexibility and adaptability. The knowledge base can be updated without the need to retrain the LLM. It can also leverage existing domain-specific knowledge bases to improve accuracy and explainability. The LLM remains versatile and can handle a wider range of tasks.

However, RAG has some weaknesses. It relies on the quality and relevance of the external knowledge base. Poor quality data can lead to inaccurate or misleading outputs. Additionally, it may not achieve the same level of deep specialization as a fine-tuned LLM for a specific task.


Often, businesses require using both Fine-Tuning and RAG to solve their problems. For example, they can fine-tune an LLM for a general task and then use RAG to provide additional domain-specific information during generation. To better explain this, let's take the example of a financial institution creating a Personalized Financial Advice Platform for its customers.


First, they would train an LLM through Fine-tuning on a vast dataset of financial news, market trends, and historical data. This fine-tuning would enhance the LLM's understanding of financial concepts and enable it to analyze investment options or generate personalized financial reports.


Then, they would use RAG to integrate the LLM with a knowledge base that contains user-specific financial information such as investment goals, risk tolerance, etc., and real-time market data feeds. This allows the LLM to provide tailored advice and suggestions to each user based on their unique financial situation while staying updated on market fluctuations. By using both Fine-tuning and RAG, the financial institution can provide personalized, accurate financial advice with little to no human oversight (i.e. automatically) while ensuring transparency and reasoning.


To summarize, Language Models (LLMs) can be a powerful tool that can be customized to suit your business needs, enabling you to take your business to the next level. You don't have to create your own LLM; instead, you can train an existing model, which is a more cost-effective and efficient approach. Below, you will find two tables: Table 1 summarizes Fine-tuning and Rag, while Table 2 highlights the Ten LLM Business Use-cases that may be relevant to your business.If you are looking to incorporate AI into your business, such as LLMs, or if you have a unique problem that requires bold solutions, we encourage you to reach out to Pipemind. We are experts in this field and would love to assist you with your business needs.


Table 1: Summary of Fine-Tuning & RAG

Feature

Fine-Tuning

RAG (Retrieval-Augmented Generation)

What it is

Internal modification of an LLM

Provides external information to an LLM

How it works

Trains LLM on new, task-specific data

Retrieves relevant info from external knowledge base

Strengths

Highly specialized performance

Flexible, adaptable, leverages existing knowledge

Weaknesses

Requires significant resources, less versatile, permanent changes

Relies on quality of external data, may not be as specialized

Use When

Need a super-specialized LLM for a single task

Need a flexible solution with existing knowledge for various tasks

Don't Use When

Resources or expertise for custom data sets are limited

Highest level of domain-specific accuracy is crucial

Business Use Case

Legal Document Review: Fine-tune an LLM to analyze legal contracts, highlighting potential issues based on a custom dataset of past legal cases.

Customer Service Chatbot: Use RAG to empower a chatbot with access to product manuals, FAQs, and support documents for more accurate and informative responses.

Table 2: Ten LLM Business Use-Cases

Use Case

Description

Example

Customer Service Automation

Automating responses to customer inquiries via chatbots or email, enhancing customer service efficiency and availability around the clock.

An online retailer could use an LLM to power a chatbot that answers customer questions about products, shipping, and returns.

Content Creation and Curation

Generating high-quality, relevant content for websites, blogs, marketing materials, and social media posts.

A marketing agency employs an LLM to create diverse content, from blog posts on industry trends to engaging social media updates.

Language Translation

Providing real-time, accurate translations to facilitate international communications and content localization.

A multinational corporation uses an LLM to translate internal communications and product documentation across its global offices.

Data Analysis and Insights

Analyzing large volumes of text data (customer feedback, market research) to extract actionable insights, trends, and patterns.

A consumer goods company uses an LLM to analyze customer reviews and feedback across various platforms to inform product development and marketing strategies.

Personalized Recommendations

Generating personalized content, product, and service recommendations for users based on their preferences and behavior.

An e-commerce platform utilizes an LLM to craft personalized email marketing campaigns that recommend products based on past purchases and browsing behavior.

Document Summarization

Creating concise summaries of long documents, reports, or articles to save time and highlight key information.

A legal firm uses an LLM to summarize case files and legal documents, enabling quick review and decision-making.

Sentiment Analysis

Analyzing customer sentiment in reviews, social media, and other text sources to gauge public opinion and customer satisfaction.

A hotel chain employs an LLM to monitor and analyze customer reviews across various platforms, identifying strengths and areas for improvement.

Code Generation and Assistance

Assisting developers by generating code snippets, debugging, or providing programming suggestions.

A software development company uses an LLM to generate boilerplate code, suggest improvements, and debug existing code.

Interactive Learning and Training

Creating interactive, adaptive learning materials and courses that can answer student questions and provide explanations in real-time.

An online education provider leverages an LLM to create a virtual tutor that can guide students through course material with personalized feedback and support.

Automating Routine Paperwork and Reports

Generating routine business reports, forms, and documents, streamlining administrative tasks.

An accounting firm uses an LLM to automatically generate tax reports and filings for clients based on financial data inputs.


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