The Understanding Large Language Models (LLM) in Business training demystifies the technologies behind modern conversational assistants such as ChatGPT, Claude, Gemini and Copilot. It gives participants a clear understanding of what an LLM is, how it works, what it can realistically achieve and where its limitations lie in a professional context.
A strategic training to move from a vague understanding to practical knowledge
During this training, participants discover how large language models produce their responses, why they can be powerful for certain tasks yet less reliable for others, and how to integrate them thoughtfully into business processes. The course also covers the concepts of context, response quality, hallucination, confidentiality and governance. It serves as an excellent foundation before more hands-on training on ChatGPT, prompts, AI assistants or enterprise deployment projects.
Module 1: Introduction to Large Language Models
Module 2: Understanding How an LLM Works
Module 3: What LLMs Do Well
Module 4: What LLMs Cannot Do Well on Their Own
Module 5: LLM Use Cases in Business
Module 6: Limitations, Risks and Key Considerations
Module 7: Integrating LLMs with Discernment
Large language models, or LLMs, are at the heart of the generative AI revolution. GPT, Claude, Gemini, LLaMA and Mistral are all LLMs trained on massive volumes of text to understand and generate human language. Understanding how they work is essential for any professional who uses or plans to use these technologies.
This understanding enables better evaluation of tool capabilities, anticipation of their limitations and informed decisions about integrating them into business processes.
An LLM works by predicting the next word in a sequence, based on billions of parameters adjusted during training. The Transformer architecture, introduced in 2017, made it possible to process long text sequences in parallel, paving the way for today’s models.
Training occurs in two main phases. Pre-training exposes the model to vast text corpora to acquire general knowledge. Fine-tuning then adjusts the model for specific tasks or domains. RLHF, or reinforcement learning from human feedback, aligns responses with user expectations.
The LLM market is dominated by a few major players. OpenAI with GPT, Anthropic with Claude, Google with Gemini and Meta with LLaMA each offer models with distinct characteristics. European players such as Mistral AI provide a relevant alternative, particularly for organisations concerned about digital sovereignty.
Each model has specific strengths. Some excel in reasoning, others in processing long documents, code generation or multilingual capabilities. Understanding these differences enables organisations to choose the most suitable model for each use case.
Adopting LLMs in business raises several strategic questions. The choice between proprietary and open-source models impacts cost, flexibility and confidentiality. On-premise versus cloud deployment determines the level of data control. Managing hallucinations and bias requires adapted verification processes.
Swiss companies must also consider regulatory aspects related to data protection and compliance with the current legal framework. A solid understanding of LLMs allows organisations to approach these questions with discernment.
Integrating LLMs into an organisation starts with identifying high-value use cases. Training teams, defining usage guidelines and establishing appropriate governance are the key steps to successful adoption.
ITTA offers this training in Geneva and Lausanne to give decision-makers and professionals the understanding they need to evaluate, select and deploy LLM-based solutions within their organisations.
Switzerland holds a privileged position in the global AI landscape. Federal polytechnic schools, research centres and innovative companies across the country are actively contributing to AI technology advances. Geneva hosts several international organisations working on AI regulation and ethics, giving Swiss professionals a unique perspective on global challenges.
For businesses in French-speaking Switzerland, AI training represents a strategic investment. Proximity to European decision-making centres, a high-quality workforce and a strong local technology ecosystem provide favourable conditions for adopting these technologies. AI-trained professionals are in particularly high demand on the Swiss job market, where demand far exceeds the available talent pool.
What is the difference between an LLM and traditional AI?
An LLM specialises in natural language processing. It understands and generates text, unlike traditional AI systems that may be designed for computer vision, speech recognition or process optimisation.
Are open-source LLMs as capable as proprietary models?
Open-source LLMs such as LLaMA and Mistral have made considerable progress. For many use cases, they deliver comparable performance to proprietary models, with the added benefit of on-premise deployment.
Can an LLM learn new information after training?
An LLM does not update its knowledge in real time. However, techniques such as RAG (Retrieval-Augmented Generation) allow it to access up-to-date information at query time, which largely addresses this limitation.
Why do LLMs sometimes produce incorrect answers?
Hallucinations occur because the model generates statistically probable text rather than factually verified content. This is an inherent aspect of how they work and requires systematic human verification.
How do you choose the right LLM for your company?
The choice depends on the use case, data volume, confidentiality requirements and budget. The training provides the evaluation criteria needed to objectively compare the available options.
Monday to Friday
8:30 AM to 6:00 PM
Tel. 058 307 73 00
ITTA
Route des jeunes 35
1227 Carouge, Suisse
Monday to Friday, from 8:30 am to 06:00 pm.
