ConsultingServices.aiAI Consulting for SMEsDemystifying AI Terms
LLM, RAG, agents? Here I translate the technical jargon into tangible descriptions. Briefly explained what it means – and why it is important for your company.
If you are choosing a system
The glossary defines the terms. The guide shows which system makes sense in which scenario.
A team of specialized AI agents, each taking on a specific role (e.g., researcher, writer, quality checker) and working together on a large project.
The highest evolutionary stage for automating entire business processes. Ideal for massive, complex projects that would normally require a human team.
An AI assistant that breaks tasks into multiple steps, independently operates tools (web search, calculator, CRM), and adjusts plans until the goal is achieved.
The step from chatbot to automation. An agent can conduct research, compile data, and take on complex tasks. It remembers previous steps.
An AI assistant that is directly integrated into existing software (like Microsoft 365, Teams, or development environments) and assists the user.
Supports employees in their daily work right where it happens, without having to switch between different tools.
An internal instance of a language model (e.g., powered by Microsoft Azure) that is completely shielded from the public internet.
Ensures full data protection and GDPR compliance. Your company and customer data never flow into the training of public models.
The umbrella term for all AI systems that can create new content (texts, images, code, or speech) – as opposed to classical AI, which only analyzes data.
The technology that suddenly makes automation tangible for SMEs because it understands language and unstructured data (like free-flowing text in emails).
Well-defined safety rules and system boundaries within which an AI is allowed to operate. They prevent an AI from deviating from the topic, giving unwanted answers, or performing unsafe actions.
Your insurance against "AI accidents." Guardrails ensure that your AI (e.g., in customer support) always acts professionally, does not promise imaginary prices, and strictly adheres to your company policies.
When an AI invents plausible-sounding but factually incorrect information because it lacks specific knowledge.
The biggest risk in business use. Reduced to nearly zero through good setup (like RAG) and clear guardrails.
Gigantic cloud providers like Microsoft (Azure), Google, or AWS that operate the massive data centers for AI models.
They provide the raw power. For tailored SME solutions (like RAG bots), consultants build on their secure infrastructure without becoming dependent on ready-made, rigid "out-of-the-box" licenses.
A pure, intelligent text generator. You ask a question, it generates the answer based on its learned world knowledge (e.g., ChatGPT).
Ideal for quick text creation, idea generation, or fast explanations. Operates autonomously without connection to external systems and does not access current company documents.
The art of giving the AI precise and context-rich instructions (prompts) so that the result is immediately usable.
Those who give the AI vague tasks receive vague results. With well-formulated system prompts, the AI works like a well-instructed employee.
A method where the AI specifically searches in a predefined database (your company documents) before formulating an answer.
Prevents fabricated answers. The AI uses only your approved knowledge (e.g., PDFs, guidelines). Perfect for company knowledge databases and reliable internal systems.
The "true total costs" of an IT solution. Not just the monthly license or API price, but also implementation, maintenance, internal training, and operation.
The most important KPI in AI procurement: An open-source model may not have a license fee, but can be significantly more expensive in operation and maintenance (high TCO) than the API call of a paid hyperscaler model.
Let's discuss in the AI initial consultation where we can most sensibly implement which technology in your daily routine.
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