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What is a Large Language Model?
A Large Language Model (LLM) is a language model with a very large number of parameters that has been trained on massive amounts of text. For every input, it predicts the statistically most likely next word—thereby generating coherent responses, translations, or summaries.
Technically, today’s LLMs are based on the Transformer architecture and are trained on billions of text examples. Well-known examples include GPT (OpenAI/Azure OpenAI), Claude (Anthropic), Gemini (Google), and open-source models such as Llama or Mistral.
LLMs are not knowledge repositories in the traditional sense. They “know” only what they have seen during training. Current or company-specific information is incorporated during productive use via RAG, function calling, or targeted fine-tuning.
For businesses, LLMs are the central technological foundation of modern AI applications: from AI chatbots to copilots to autonomous agents. The crucial question is rarely “which model?” but rather “how securely, how data-efficiently, and how well integrated will we deploy it?”
Best Practices for Working with LLMs
LLMs only deliver their full value when they are operated properly. Six principles have proven effective:
- Choose a model based on the task: Not every problem requires the largest model—often an SLM is sufficient.
- Privacy first: Process personal data and trade secrets only through GDPR-compliant instances (e.g., Azure OpenAI).
- RAG instead of fine-tuning: In most cases, RAG is faster, cheaper, and easier to maintain.
- Monitor costs: Actively measure token and latency costs. Otherwise, you’ll be in for a surprise when the bill arrives.
- Treat prompts as assets: Store proven prompts as standards, versioned and tested.
- Test model changes: First compare new versions to the current state using evaluations.
Common Mistakes in LLM Deployment
We see these mistakes time and time again in companies—and they’re easily avoidable:
- “One model for everything”: An LLM is not a search engine, a database, or a calculator. Choose the right tool for the job.
- No Governance Strategy: Anyone who uses LLMs without defined roles, approval processes, and logs risks compliance issues and reputational damage.
- Lack of Context: Without RAG or clear prompts, the model will make things up—and is highly likely to do so in an inappropriate tone.
- No cost monitoring: Large contexts and long responses can drive costs to unexpected levels.
- Model changes without testing: New versions may behave differently. Evaluations protect against regressions.
LLM vs. SLM vs. Foundation Model
These terms are often used interchangeably, but they differ significantly:
- Foundation Model: A base model that has been extensively pre-trained and serves as the foundation for many tasks.
- Large Language Model: A foundation model for language tasks—with a very large number of parameters.
- Small Language Model: A more compact model—faster, more cost-effective, and often tailored to a specific use case.
Using LLMs Safely and Productively in Your Organization
In a free initial consultation, we’ll review your use cases and work with you to select the right model, architecture, and operational setup.
As an AI partner for small and medium-sized businesses, we bring large language models from the experimental stage into your day-to-day operations—securely, in compliance with the GDPR, and cost-effectively.
What We Offer
- AI consulting for SMBs —from model selection to operational strategy.
- Microsoft Copilot Consulting — LLMs integrated directly into your Microsoft 365 work environment.
- AI chatbots for businesses —LLM-based assistants for service and sales.
- Guide: AI Strategy for SMEs — free download from the prodot media library.
