AI Guides

An AI agent is an AI system that plans its own steps toward a goal and carries out the work using tools or software. A chatbot answers a question and stops; an agent can, for example, read an incoming request and open the related record or prepare a report. That autonomy means permission limits and the steps that need human approval have to be designed from the start.

An LLM (large language model) is an AI model trained on a very large amount of text that can understand and generate language; ChatGPT, Claude and Gemini are built on such models. The model reads text and produces the most likely continuation, word by word, based on patterns learned in training. That is why it predicts plausible text rather than verifying facts.

RAG (retrieval-augmented generation) is a method that finds the relevant documents for each question and gives them to the language model as context. The model answers from the sources it receives at that moment rather than from its own memory. It is the most common and cost-effective approach for assistants that work with frequently updated information such as internal documents, product catalogs or support tickets.

Fine-tuning retrains an existing model on your own example data and changes its behavior permanently. RAG only supplies the model with information, while fine-tuning teaches it a specific tone, output format or domain language. RAG fits best when information changes often, fine-tuning when you need consistent behavior, and the two can be combined.

A hallucination is a fluent, convincing but wrong or invented answer. Language models do not verify facts; they generate the most likely text, so when information is missing or ambiguous they can fill the gap with a guess. You reduce the risk by grounding the model in reliable sources (RAG), asking it to cite them and having people review critical output.

Integration usually starts with a single business process: first define the problem AI should solve and how success will be measured, then choose the data and model. The model is connected to existing software through an API, tested in a small pilot and its results are measured. Data privacy, permissions and the steps that need human approval should be part of the design from the start.