Recent statistics have shown that the adoption of artificial intelligence (AI) in business is increasing rapidly. About 64% of businesses believe AI will increase productivity. Another 39% of businesses also hired software engineers for roles involving AI in 2022 alone.

Where is AI finding use? Organizations are using AI in various ways, such as customer service, data analysis, and workflow automations. These outcomes are achieved by training AI on in-house data and creating tools such as an AI-powered knowledge base.

If your organization is thinking of capitalizing this opportunity, this guide is for you. We highlight how to leverage generative AI capabilities for business applications through proper training.

How to Train a Generative AI Model: Essential approaches

Artificial intelligence training focuses more on using algorithms to mimic human behavior. A conversational AI model can interact with an end user in a human-like way. This means that users don't need any programming or data science experience to use generative AI applications.

This is different from traditional machine learning methods that involve creating algorithms to process data. A machine learning model is used to describe or predict patterns in the training data. End users may need data science knowledge to interpret ML results.

There are three main ways of training large language models (LLMs) for AI applications today:

1. Building a model from scratch

An organization can choose to create and train a custom LLM based on their own data. This process requires huge volumes of high-quality data and considerable computing power.

Although this approach offers the most flexibility for a business, it is also the most expensive. Based on our experience, an LLM can rack up costs such as:

  • Graphics processing units (GPUs) and related hardware
  • Power consumption (up to 1 gigawatt per day)
  • Salaries for infrastructure engineers and data scientists.

As you may already have imagined, this approach is suitable for well resourced organizations who can comfortably make this investment.

2. Training an existing model

Alternatively, you can choose to train an existing LLM with company data. The most popular LLMs today include:

Developers can fine tune the LLMs to perform specific tasks using in-house data. For example, ChatGPT can be trained on a company's marketing data to generate reports or predict customer trends. Developers need access to the LLM's application programming interface (API) to tweak the base model.

3. Creating custom prompts

In this approach, developers use prompt tuning to get the answers they seek from the training data.

A prompt is a piece of text that tells the AI what to do using normal everyday language. It can be a question or an instruction. Prompt tuning means modifying the prompts rather than the base model. Developers can include contexts or roles in the prompts to get the information they need from the LLM.

Let’s say you are an e-commerce company, for example. You can train your AI solution to respond like a customer service agent. The virtual agent can then offer troubleshooting advice tailored to your products.

Generative AI models training phases

Training for generative AI can be a long and complex process. But what's most important for you as a company is to get it right. To get it right, the whole exercise can be broken down into the following phases:

1. Pre-training phase

The initial phase of generative AI model training involves collecting and preparing the training content. This means:

  • Gathering a sample data set that represents business data, e.g., studies, records, or other materials
  • Gathering image generation content in various styles and categories
  • Standardizing raw data into the correct formats, e.g., converting PDFs into text files.

When the training content is ready, developers link it to the base model to begin the next phase.

2. Training phase

The training phase involves allowing the models to interpret training content and achieve the desired outcome. It also includes setting the right metrics to monitor the model's generative AI capabilities with the goal of ensuring that it is working as intended and that it can keep improving over time.

Using image generation as an example, generative AI training means:

  • Creating new images that look exactly like the training images
  • Differentiating between training images and generated images
  • Classifying training and generating images into "real" and "fake" images.

Over time, the AI creates images that are indistinguishable from the training content. The same training process applies to generating text and voice content.

3. Post-training phase

In this final phase, developers compare the model's outcomes against a validation data set. A validation data set is a separate data set that is not used in the training content. The purpose of this data is to help evaluate the model's performance objectively.

For example, you can use the best-performing customer service guide as the validating data set. The generative AI applications should produce new guide materials that match or even surpasses the validation set in terms of style, value, and engagement.

It is important to note that training for generative AI is a continuous process. It also requires human moderators at each stage. While at it, developers need to watch out for conversational AI results that offer misleading information or divulge sensitive data.

Best practices for proper training for generative AI models

Please apply the following best practices to ensure the highest quality of training and achieve the right outcomes,:

  • Define clear objectives and metrics for training
  • Use a balanced, diverse data set to avoid biased results
  • Use real data that represents real-world business scenarios
  • Apply human expertise and value judgments on AI-generated results
  • Secure the data both on-premise and in cloud LLMs
  • Continuously adjust the generative AI model to match changing business needs.

Conclusion

There are numerous benefits for businesses that use generative AI capabilities. For example, you will easily streamline your human resource operations by automating employee onboarding and evaluations. You can also create better customer-facing and internal applications through automating the development process. These are just a few examples.

However, the World Economic Forum highlights the need for businesses to be responsible in their use of AI. Even as you take the initiative to utilize generative AI, you should ensure that your generative AI models are:

  • Performing effectively and as intended
  • Robust and secure to avoid compromising the training content
  • Free of biases against individuals or groups
  • Up to date with privacy guidelines when handling personal or sensitive data

We particularly recommend you consider training your in-house developers through generative AI courses, such as:

These courses cover the fundamentals of training for generative AI. They also cover the ethical aspects of using AI in businesses. In-house developers can use these courses to integrate AI into business operations.

Recent statistics have shown that the adoption of artificial intelligence (AI) in business is increasing rapidly. About 64% of businesses believe AI will increase productivity. Another 39% of businesses also hired software engineers for roles involving AI in 2022 alone.

Where is AI finding use? Organizations are using AI in various ways, such as customer service, data analysis, and workflow automations. These outcomes are achieved by training AI on in-house data and creating tools such as an AI-powered knowledge base.

If your organization is thinking of capitalizing this opportunity, this guide is for you. We highlight how to leverage generative AI capabilities for business applications through proper training.

How to Train a Generative AI Model: Essential approaches

Artificial intelligence training focuses more on using algorithms to mimic human behavior. A conversational AI model can interact with an end user in a human-like way. This means that users don't need any programming or data science experience to use generative AI applications.

This is different from traditional machine learning methods that involve creating algorithms to process data. A machine learning model is used to describe or predict patterns in the training data. End users may need data science knowledge to interpret ML results.

There are three main ways of training large language models (LLMs) for AI applications today:

1. Building a model from scratch

An organization can choose to create and train a custom LLM based on their own data. This process requires huge volumes of high-quality data and considerable computing power.

Although this approach offers the most flexibility for a business, it is also the most expensive. Based on our experience, an LLM can rack up costs such as:

  • Graphics processing units (GPUs) and related hardware
  • Power consumption (up to 1 gigawatt per day)
  • Salaries for infrastructure engineers and data scientists.

As you may already have imagined, this approach is suitable for well resourced organizations who can comfortably make this investment.

2. Training an existing model

Alternatively, you can choose to train an existing LLM with company data. The most popular LLMs today include:

Developers can fine tune the LLMs to perform specific tasks using in-house data. For example, ChatGPT can be trained on a company's marketing data to generate reports or predict customer trends. Developers need access to the LLM's application programming interface (API) to tweak the base model.

3. Creating custom prompts

In this approach, developers use prompt tuning to get the answers they seek from the training data.

A prompt is a piece of text that tells the AI what to do using normal everyday language. It can be a question or an instruction. Prompt tuning means modifying the prompts rather than the base model. Developers can include contexts or roles in the prompts to get the information they need from the LLM.

Let’s say you are an e-commerce company, for example. You can train your AI solution to respond like a customer service agent. The virtual agent can then offer troubleshooting advice tailored to your products.

Generative AI models training phases

Training for generative AI can be a long and complex process. But what's most important for you as a company is to get it right. To get it right, the whole exercise can be broken down into the following phases:

1. Pre-training phase

The initial phase of generative AI model training involves collecting and preparing the training content. This means:

  • Gathering a sample data set that represents business data, e.g., studies, records, or other materials
  • Gathering image generation content in various styles and categories
  • Standardizing raw data into the correct formats, e.g., converting PDFs into text files.

When the training content is ready, developers link it to the base model to begin the next phase.

2. Training phase

The training phase involves allowing the models to interpret training content and achieve the desired outcome. It also includes setting the right metrics to monitor the model's generative AI capabilities with the goal of ensuring that it is working as intended and that it can keep improving over time.

Using image generation as an example, generative AI training means:

  • Creating new images that look exactly like the training images
  • Differentiating between training images and generated images
  • Classifying training and generating images into "real" and "fake" images.

Over time, the AI creates images that are indistinguishable from the training content. The same training process applies to generating text and voice content.

3. Post-training phase

In this final phase, developers compare the model's outcomes against a validation data set. A validation data set is a separate data set that is not used in the training content. The purpose of this data is to help evaluate the model's performance objectively.

For example, you can use the best-performing customer service guide as the validating data set. The generative AI applications should produce new guide materials that match or even surpasses the validation set in terms of style, value, and engagement.

It is important to note that training for generative AI is a continuous process. It also requires human moderators at each stage. While at it, developers need to watch out for conversational AI results that offer misleading information or divulge sensitive data.

Best practices for proper training for generative AI models

Please apply the following best practices to ensure the highest quality of training and achieve the right outcomes,:

  • Define clear objectives and metrics for training
  • Use a balanced, diverse data set to avoid biased results
  • Use real data that represents real-world business scenarios
  • Apply human expertise and value judgments on AI-generated results
  • Secure the data both on-premise and in cloud LLMs
  • Continuously adjust the generative AI model to match changing business needs.

Conclusion

There are numerous benefits for businesses that use generative AI capabilities. For example, you will easily streamline your human resource operations by automating employee onboarding and evaluations. You can also create better customer-facing and internal applications through automating the development process. These are just a few examples.

However, the World Economic Forum highlights the need for businesses to be responsible in their use of AI. Even as you take the initiative to utilize generative AI, you should ensure that your generative AI models are:

  • Performing effectively and as intended
  • Robust and secure to avoid compromising the training content
  • Free of biases against individuals or groups
  • Up to date with privacy guidelines when handling personal or sensitive data

We particularly recommend you consider training your in-house developers through generative AI courses, such as:

These courses cover the fundamentals of training for generative AI. They also cover the ethical aspects of using AI in businesses. In-house developers can use these courses to integrate AI into business operations.