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6 minute read
30 Jan 2025

Everything You Need to Know About LLM Training for Your Business

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For business

More and more companies see the value of integrating AI into their operations. Integrating Large Language Models (LLMs) into day-to-day operations can deliver powerful benefits, such as streamlined processes, data-driven insights, and improved knowledge sharing. However, the success of LLMs depends on high-quality data. This is where transcription services become essential, transforming spoken content such as meetings, calls, and interviews into valuable LLM training data. In this article, you’ll learn how to train LLMs to meet your business needs effectively, why data quality is critical, and how transcription can help you train your AI model.

Table of Contents

  • What are LLMs?
  • How are LLMs trained?
  • Why train AI models for your business?
  • How transcription helps with AI model training
  • How to train LLM on your own data in 3 steps
  • Start training your AI model with accurate, high-quality transcriptions

What Are LLMs?

A Large Language Model (LLM) is a machine-learning model that understands, generates and manipulates human-like text. These programs are trained on massive datasets, hence the name ‘large’, to understand how language works and to generate text by finding and storing text patterns. LLM uses deep learning to understand how characters, words, and sentences work together, resulting in an AI model that generates, for example, answers, content, translations, and summaries.

How Are LLMs Trained?

Before discussing why LLM AI model training benefits your business, it’s good to know how LLM training works. Below is a brief explanation of the steps in the AI model training process:

  1. Data collection: First, a large amount of text data is collected, cleaned, and prepared for the model to learn from.
  2. Model configuration: Most LLMs use a type of neural network called a transformer, which helps the model understand how words relate to each other in large texts. During this step, important settings (called parameters) are defined.
  3. Training the model: The model is trained by predicting missing words or words in the next sentence. It compares its prediction with the actual data and learns from its mistakes to improve over time.
  4. Fine-tuning: After training, the model can be adjusted and improved to provide more accurate and relevant results for specific tasks or industries. This can be done through reinforcement learning, where the model is further refined using human feedback and reward signals.

From Unsupervised Learning to Supervised Learning

LLMs typically start with unsupervised learning to develop a broad understanding of patterns, structures, and relationships within the text. Supervised learning is then used to fine-tune the model for specific tasks, improving accuracy and relevance.

Why Train AI Models for Your Business?

Training AI models for your business can transform the way your organisation operates. It brings many benefits that drive more efficiency, innovation, and growth. Benefits include:

  • Industry-specific knowledge: The model understands your industry jargon, regulations, and workflows, leading to more accurate, relevant outputs and results in smoother communication.
  • Process automation: The model can automate business-specific tasks such as answering the most common customer questions, improving efficiency and reducing operational costs. Your team can focus on complex issues while AI handles the routine queries.
  • Internal knowledge sharing: The model can streamline internal knowledge sharing, helping your team access the right data much faster. This leads to better decision-making and a more efficient workflow.
  • Multilingual and regional customisation: For global businesses, custom models can be trained to handle multiple languages, dialects, and cultural nuances specific to your market.

You Probably Already Own the Perfect Training Data

You might be thinking, nice AI model training, but how do I get the right data to make the model fit my organisation? Chances are you already have this data, such as call logs or training videos. By using this existing information related to your company, you can effectively train your AI models. The model learns from real interactions, becoming an invaluable asset that evolves with your business needs.

How Transcription Helps With AI Model Training

As you already read above, AI models are trained with large amounts of text, so you basically need text to help the model understand language patterns. In fact, data is the foundation of effective AI model training.

A powerful way to enrich data sets is through transcription, which converts spoken content, such as meetings, interviews, and podcasts, into structured text. This process transforms audio data into valuable, searchable resources that can be used to train AI models. You can create your own transcriptions from any available source, but it is faster to use a transcription service to do it that can create high-quality, valuable text for the AI model. Below are two examples of how you can use transcription for your AI training efforts:

1. Improve Customer Support

You can use call transcripts to train AI chatbots or virtual assistants to understand customer interactions better. Let us give you an example. Suppose a significant number of customers contact your customer service department regarding billing issues. Customer service agents may find themselves overwhelmed with the task of answering all the calls. This process can be streamlined by having your AI tool analyse these call logs to learn common questions and answers. This enables it to address similar concerns quickly and accurately, resulting in a more efficient support system.

2. Store Company Knowledge

Another example of using transcription to train your AI model is to store company knowledge. During internal meetings, training sessions and other forms of interaction, a lot of information is shared verbally, which can be lost if not properly stored. By transcribing internal conversations, you can create a comprehensive, searchable knowledge base for your employees. They can easily access past interactions to make informed decisions, fostering a culture of knowledge sharing and collaboration between teams.

How to Train LLM on Your Own Data in 3 Steps

Training a Large Language Model for your business may sound like a difficult or long-term task. Indeed, factors such as model complexity can affect the time it takes to train an AI model, but it doesn’t have to be difficult. By following a few steps, you can harness the power of Large Language Models to improve your operations and decision-making. Let’s break it down into manageable actions:

1. Set a Goal

The first step is to set a goal using the SMART approach. Start by identifying what you want to achieve with your AI model. Do you want better customer support, a smarter internal knowledge system, or perhaps more insights from your data? With a clear goal in mind, you can tailor your approach and measure success effectively.

2. Gather Data

The next step, of course, is to gather relevant and accurate data. This can include a variety of sources, such as documents, chat logs, and transcripts of calls or meetings. By compiling diverse data sets, you ensure that your model has a rich foundation from which to learn. Remember that the quality and relevance of your data will significantly impact the model’s performance, as it equips your model with the necessary context and nuance to understand and generate human-like responses. 

3. Train Your Model

Finally, it’s time to train the AI model. You can refine an existing AI model your company already uses. Or you can start from scratch, depending on your needs and resources. This process involves feeding your collected data into the model, allowing it to learn patterns and make predictions based on the information provided.

Start Training Your AI Model With Accurate, High-Quality Transcriptions

Now that you understand the value of training your Large Language Model AI model and the benefits it can bring to your business, it’s time to take action. By using accurate, high-quality transcriptions, you can turn spoken content into powerful LLM training data that will improve the LLM’s performance. At Amberscript, we are committed to helping you by creating fast and precise transcripts, tailored to your needs. Start today to unlock the full potential of AI in your operations and drive your business forward.

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