The challenge: You ask a specific question about a complex B2B product and receive a link to the FAQ homepage as an answer. Frustrating. Inefficient. Generic. The next generation of AI assistants in companies are not satisfied with this. They not only understand the question, but also who is asking it, in which context and for which use case. The goal: a sales manager receives a different answer to the question "How is the performance?" than an IT administrator or an end customer. The result is individually tailored information instead of generic text modules.
But how does this work? How do you turn a static language model (LLM), which basically only calculates probabilities for the next word, into a context-sensitive expert?
The answer does not lie in a single "magic algorithm", but in a sophisticated architecture that orchestrates various technical components. We take a look under the hood of our personalization engine.
The foundation: the multidimensional context engine
A conventional chatbot often only sees the current "prompt" (the input). A personalized bot, on the other hand, operates in a three-dimensional space consisting of user identity, situational context and company knowledge.
In order to generate dynamic responses, three technical pillars must interlock seamlessly before the actual language model (LLM) even formulates a response.
- Pillar 1: Identity & Role Awareness (the "who"). The basis of every personalization is knowledge about the enquirer. In technical terms, this means deep integration into the company's Identity and Access Management (IAM) (e.g. Active Directory, Okta). The chatbot not only knows the name of the user, but also their role, department and authorization level. This metadata is carried "invisibly" with every request. The technical lever: this metadata serves as a filter (pre-filtering) for the knowledge base. A "Junior Sales Agent" technically has no access to the documents in the index that are marked for "C-Level Executives". The LLM cannot disclose any information that it is not allowed to "see" in the first place.
- Pillar 2: Conversational Memory & Situational Context (The "Now"). Personalization also means remembering. A user should not have to explain anew in every message what it is about. Technically, we solve this using vector databases (vector stores) as long-term memory and specialized session handlers as short-term memory. The bot stores the previous course of the conversation not only as text, but also as semantic vectors (carriers of meaning). If a user asks: "And how does it look compared to the previous year?", the bot understands what "it" refers to (e.g. the turnover in the DACH region that was just discussed) thanks to the stored context.
- Pillar 3: Advanced Retrieval-Augmented Generation (RAG) (the "what"). This is the heart of dynamic content. RAG is the technique where the LLM not only draws on its trained knowledge, but actively searches external, company-specific documents to generate an answer. However, "standard RAG" is not enough for true personalization. We use context-based RAG. In simple terms, the process looks like this:
- The query comes in: "How do I change the configuration?"
- Context enrichment: The system recognizes that the user is an "IT admin" (role), is in the "Server security" module (use case) and has previously asked about firewall rules (history).
- Targeted retrieval: The system now searches the knowledge database not generally for "Change configuration", but specifically for documents that are relevant for IT admins in the area of server security.
- Dynamic prompting: Now the magic happens. The system assembles a complex command (prompt) for the LLM. This looks something like this for the model:
- "You are a technical assistant. Answer the user's (role: IT admin) question about configuration in the context of server security. Use ONLY the following three technical documents as a source [document A, B, C]. Be technically precise and brief in your answer, as an admin would expect."
- If the same question had come from a marketing manager, the system would have retrieved other documents and told the LLM in the prompt to formulate the answer "easy to understand and benefit-oriented".
The result: Tailored relevance
The combination of role awareness, conversational memory and context-driven RAG fundamentally changes the way information is consumed. It is no longer a question of whether the bot finds an answer, but how it presents it. The technical background may be complex, but the goal is simple: to give the user exactly the information they need right now - in the language they understand.
That's the difference between a search engine with a chat window and a real digital colleague.