ConsultingServices.aiKI-Consulting für KMUAI Terms Demystified
LLM, RAG, Agents? Here I translate the technical jargon into tangible descriptions. Brief explanations of what it means – and why it matters to your business.
A team of specialized AI agents, where each takes on a specific role (e.g., researcher, copywriter, quality checker) and works 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 down tasks into multiple steps, autonomously operates tools (web search, calculator, CRM), and adapts 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 work steps.
An AI assistant built directly into existing software (like Microsoft 365, Teams, or development environments) to assist the user.
Supports employees in their daily work right where it happens, without them having to switch between different tools.
An internal corporate instance of a language model (e.g., operated via Microsoft Azure) that is completely shielded from public networks.
Ensures full data protection and GDPR compliance. Your company and customer data will never flow into the training of public models.
The umbrella term for all AI systems that can create new content (text, images, code, or speech) – as opposed to traditional AI, which only analyzes data.
The technology that suddenly makes automation tangible for SMEs because it understands language and unstructured data (like free-flowing texts in emails).
Strictly defined safety rules and system boundaries within which an AI must operate. They prevent the AI from drifting off-topic, giving undesired answers, or executing unsafe actions.
Your insurance against "AI accidents". Guardrails guarantee that your AI (e.g., in customer support) consistently acts professionally, does not promise fantasy prices, and strictly adheres to your corporate guidelines.
When an AI invents plausible-sounding but factually incorrect information because it lacks specific knowledge.
The biggest risk in business use. This is reduced to almost zero using proper setup (like RAG) and clear boundaries (Guardrails).
Gigantic cloud providers like Microsoft (Azure), Google, or AWS that operate the massive data centers required for AI models.
They provide the raw power. For tailor-made SME solutions (like RAG bots), a consultant builds on their secure infrastructure without falling into the dependency of rigid "out-of-the-box" licenses.
A pure, intelligent text generator. You ask a question, and it generates the answer based on its learned world knowledge (example: ChatGPT).
Ideal for rapid text creation, brainstorming, or quick explanations. Operates autonomously without integration into external systems and does not access current corporate documents.
The art of giving the AI precise and context-rich instructions (prompts) so that the result is immediately usable.
If you give the AI vague tasks, you get vague results. With properly formulated system prompts, the AI works like a well-instructed employee.
A method where the AI specifically searches a predefined database (your company documents) before formulating an answer.
Prevents invented answers. The AI exclusively uses your approved knowledge (e.g., PDFs, guidelines). Perfect for enterprise knowledge bases 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 when acquiring AI: While an open-source model has no license fee, its operation and maintenance (high TCO) can be significantly more expensive than the API calls of a paid Hyperscaler model.
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