Understanding using AI and LLM Models in your business

Artificial intelligence (AI) and large language models (LLMs) are reshaping how businesses operate, offering the ability to automate tasks, generate detailed insights, and handle complex queries with ease. However, many business owners mistakenly believe that to harness AI’s power, they need to build custom models from the ground up. In reality, most businesses can achieve significant results by adjusting existing models to suit their specific needs, without starting from scratch.

This is particularly relevant in industries like construction, where access to specialized knowledge (e.g., building codes, safety regulations, project management best practices) is critical. In this article, we'll explore how your business can leverage and customize existing AI models to drive efficiency and innovation.

Customization of existing models

Instead of building your own AI model, you can customize existing LLMs like OpenAI’s GPT-4, BERT, or similar models from cloud providers such as Microsoft Azure. These models come pre-trained on vast datasets, giving them a broad understanding of language, but you can refine them to meet your business-specific needs through the following approaches:

1. Prompt engineering and API integration

Prompt engineering is the simplest method to customize an LLM for your business. Here, instead of retraining or adjusting the model’s underlying architecture, you customize the input you give it — the prompt — to generate tailored results. By providing a clear, structured prompt, the LLM can produce outputs that match the specific context of your industry.

For instance, in construction, you can guide the LLM with a prompt like:
"Summarize the key points of the Seattle building permit requirements for 2023, focusing on changes in energy efficiency standards."
This prompt gives the model enough context to understand the task and generate a relevant answer.

API Integration takes this a step further by embedding the LLM into your business processes. With an API, the model can automatically interact with customer queries, generate reports, or assist with documentation in real-time.

Example use cases:

• Customer support: Automatically answer common questions about project timelines, building regulations, or permit applications using a chatbot that communicates with the LLM via an API.

• Document generation: Integrate the API into your project management system to generate detailed reports or construction contracts based on input from project managers.

How it works:

Via the API, your software sends a prompt to the LLM, receives a response, and then integrates that response back into the workflow. This is ideal for businesses that want a flexible and dynamic AI tool without having to modify the model itself.

2. Fine-tuning

Fine-tuning is a more advanced customization method where the LLM is retrained with your specific business data. This process allows the model to learn the nuances of your industry, whether it’s the unique language used in contracts, regulations, or technical reports in the construction field.

Fine-tuning adjusts the model's weights based on the new data, making the model’s understanding more specific to your domain. This is particularly useful when you need the model to generate consistent, specialized outputs, such as legal documents, highly technical reports, or detailed project proposals.

Example:

A construction company can fine-tune an LLM using thousands of documents related to building codes, safety standards, or contract language. After fine-tuning, the model would be able to generate custom legal documents or respond accurately to technical queries about site regulations.

How it works:

You gather your business-specific dataset (e.g., contracts, internal documents, reports) and use that to retrain the LLM. This process usually involves several steps:

1. Data preprocessing: Cleaning and formatting your data to ensure it's suitable for training.

2. Training: Fine-tuning the pre-trained LLM with your data over multiple iterations (epochs), allowing the model to learn domain-specific language and patterns.

3. Deployment: Once fine-tuned, the model is deployed and ready to respond to inputs with the specialized knowledge it has learned.

While fine-tuning requires more technical resources and sufficient data — typically 10,000 or more relevant samples — the result is a model that can generate highly accurate, industry-specific responses.

3. Embedding Retrieval and Retrieval-Augmented Generation (RAG)

Another powerful method for customizing LLMs is embedding retrieval. This method involves storing your business data in a vector database, then allowing the model to retrieve relevant information from this database when generating responses.

Embeddings are numerical representations of text, created by transforming words, sentences, or documents into high-dimensional vectors that represent their meaning. These vectors are stored in a database and can be queried based on their similarity to an input prompt.

For example, let’s say your construction company has a database of documents containing local building codes. When the LLM receives a query like "What are the building permit requirements in Seattle?", it can retrieve the most relevant documents from the vector database, providing specific, up-to-date information in its response.

Retrieval-Augmented Generation (RAG) takes this concept further by combining the power of LLMs with real-time data retrieval. In a RAG system, when a query is made, the LLM first retrieves the most relevant documents from a vector database based on the query's embedding, and then generates an answer by combining the retrieved information with its own pre-trained knowledge.

Example:

A project manager asks, "What are the latest changes to building codes in Seattle?". The LLM retrieves up-to-date documents from the vector database and generates a response that combines this external knowledge with its own understanding of construction-related regulations.

How it works:

• Embedding model: Convert your documents into vector embeddings and store them in a vector database (e.g., Azure Cosmos DB with vector search, or FAISS).

• Querying: When a query is made, the model converts the query into an embedding and retrieves the most semantically similar documents from the database.

• Response generation: The retrieved documents are included in the prompt as context, allowing the LLM to generate an accurate response based on your business data and any pre-trained knowledge.

This method is ideal for dynamic information retrieval, ensuring that the LLM uses the most current and relevant data to assist with specific tasks, without requiring a full fine-tuning process.

Cloud services to use

Several cloud services make it easy to integrate and customize LLMs without needing in-house infrastructure. Services like Azure OpenAI Service, AWS SageMaker, and Google Cloud AI provide the computational resources, pre-trained models, and APIs necessary to implement LLMs in your business.

For example:

• Azure OpenAI service: Allows you to access models like GPT-4, fine-tune them with your data, and integrate them into applications through APIs.

• AWS SageMaker: Provides tools for fine-tuning and deploying LLMs with built-in support for popular models like GPT and BERT.

• Google Cloud AI: Offers services for fine-tuning and embedding retrieval, making it easier to customize models with your data.

Using these cloud services means you don't have to worry about managing hardware, scaling your model, or maintaining infrastructure — everything is handled by the cloud provider.

Limitations and benefits

While customizing LLMs can significantly enhance your business processes, there are some limitations to be aware of:

• Data requirements: For fine-tuning to be effective, you’ll need a large, high-quality dataset. Small datasets may not provide enough information for the model to learn, limiting the impact of the fine-tuning process.

• Computation and costs: Fine-tuning requires significant computational resources, which can be expensive depending on the size of the model and the amount of data you have.

However, the benefits are substantial. Customized LLMs can:

• Automate repetitive tasks like document generation, customer queries, or project reporting.

• Increase accuracy by using domain-specific knowledge (e.g., construction regulations).

• Save time and resources, freeing up your team to focus on high-value tasks.

For example, a construction company could fine-tune a model to automatically generate project progress reports, saving hours of manual writing and review.

Security considerations

When using cloud-based LLMs, it's natural to worry about data privacy and security. The good news is that cloud providers like Azure, AWS, and Google offer robust security measures, including encryption and access controls, to protect your data.

For fine-tuning or embedding retrieval, your data is typically stored and processed in a secure environment. Unless you opt in to share your data with the cloud provider for broader training purposes, the model won’t use your data outside of your specific project. You maintain control over your data, ensuring it stays private and secure.

Costs

The cost of customizing an LLM depends on the method you choose:

• Prompt engineering and API integration are relatively low-cost since they don't require retraining the model. You typically pay per request or token used in the API.

• Fine-tuning can be more expensive due to the need for computational power and large amounts of data. Costs vary depending on the model size and the amount of fine-tuning required.

• Embedding retrieval and RAG sits somewhere in between—requiring you to maintain a vector database, but still less resource-intensive than full fine-tuning.

In the long run, customized LLMs can provide significant ROI by automating tasks, increasing efficiency, and improving the accuracy of key business processes.

Conclusion

The days of building AI from scratch are over. Today, businesses—especially in industries like construction — can benefit greatly from customizing existing AI models to meet their specific needs. Whether through prompt engineering, fine-tuning, or embedding retrieval, there are powerful ways to adapt these models to enhance business operations.

To learn more about how we can help you integrate AI solutions into your business, visit our AI Solutions page.