Practical Guide to Using LLMs in Business

Explore our guide on using Large Language Models (LLMs) in business. Learn about top models, costs, and implementation tips for effective integration

1 - Introduction

If you are a Swiss company considering adopting a Large Language Model (LLM) for a specific use case, this guide will help you assess the costs and available options to effectively integrate these technologies into your business environment.

2 - Definition of an LLM

A Large Language Model (LLM) is an artificial intelligence model that uses vast amounts of textual data to understand, interpret, and generate human language. These models are often based on neural network architectures, such as transformers, and can handle and produce complex texts. To deepen your understanding, you may refer to resources like Deep Learning courses.

3 - LLMs for Businesses

Here are some popular models to consider for your use cases:

Llama 2: Known for its excellent performance in standard benchmarks, it offers an economical alternative to GPT-4.
Mistral 7B: Noted for its speed and efficiency, though there are concerns regarding content moderation.
GPT-3.5 and GPT-4: These models continue to represent industry standards in terms of versatility and performance, though they are not open-source.

4 - Determining Your Use

Training: This refers to the process of developing a language model from raw data. This process is resource-intensive and costly.
Fine-Tuning: This method involves adjusting a pre-trained model on a specific dataset or for a particular task. During our AI hackathon at IDIAP, we used a direct Fine-Tuning approach instead of RLHF (Reinforcement Learning Human Feedback), based on human rating. This approach allowed us to refine a Llama2 model to generate more precise and context-specific questions for recruitment at BCVS. Although simpler than RLHF, this Fine-Tuning method proved effective in creating personalized and relevant questions compared to ChatGPT.
Prompt Formulation: Creating clear and precise instructions to guide the model in generating useful and contextual responses. Good prompt formulation is crucial for obtaining relevant results.
Inference: Using the model to generate text or make predictions based on new inputs.
Training Costs: Training a model like GPT-4 is extremely costly in terms of computational resources and data, making internal training impractical for most companies.

5 - Test Models Yourself on Your Use Cases

While model benchmarks are informative, testing the models directly can provide a better understanding of their performance and applicability. Use platforms like Replicate to evaluate model outputs and make an informed choice.

6 - Considerations for LLM Deployment and Usage Costs

Cloud-Based API: Using OpenAI’s Chat GPT API. Costs are based on usage. This is a convenient option for companies seeking quick integration without the complexity of managing infrastructure.
Open-Source Platforms: Using platforms such as Azure or Hugging Face, which offer turnkey solutions for deploying open-source models. These platforms manage hosting and inference, simplifying the implementation process. Costs vary based on usage and required resources.
Hybrid Approach: Experimentation and Fine-Tuning on a platform followed by deployment. This method combines the flexibility of cloud platforms with the control of a customized deployment.
Long-Term Cost Considerations: Avoid excessive reliance on third-party APIs, as they can become expensive at scale. Monitor costs and explore alternatives if needed. Refer to AnyScale’s article for a detailed cost analysis of LLM-based applications.

7 - Data Security and Privacy

Data security is crucial. While platforms like Azure and OpenAI ensure data privacy with certifications such as SOC-2, some companies may prefer solutions that minimize data sharing with third parties. For a comprehensive understanding of data privacy management with LLMs, refer to our dedicated guide.

8 - Sigmapulse

Initial Phase: We recommend using GPT-3.5 or GPT-4 for initial proof of concept (POC) phases due to their versatility and proven performance.
Implementation Phase: For production deployment, we suggest adopting Llama2 or Falcon7B using platforms like Azure for simplified management. This approach ensures a smooth transition from testing to large-scale production.
Customization: We can also deploy and manage these models directly in your environment, providing a tailored solution suited to your specific needs.