Understanding the different technologies behind artificial intelligence

Artificial intelligence can answer questions, search corporate documents for data, or execute a sequence of tasks. At first glance, it all seems to be part of the same technology, but these functionalities are underpinned by distinct architectures and objectives.

Familiarity with key terms helps us better evaluate the tools we encounter daily and understand what a company is offering when it announces a new AI-based solution.

RAG: When AI consults external sources

RAG stands for Retrieval-Augmented Generation, a concept that enables AI to consult a database of information before generating a response.

This approach allows internal documents, manuals, or knowledge bases to be connected to the tool. When a user asks a question, the system searches for relevant excerpts in these materials and uses them to formulate the answer.

The goal is to reduce reliance on the AI’s pre-existing knowledge and enable the use of specific, up-to-date information.

Agents: AI that executes process steps

An agent receives a goal and determines the necessary actions to achieve it, consulting data, using other tools, and chaining different steps together.

For example, an agent responsible for analyzing a customer request could consult purchase history, check internal rules, prepare a response, and record the interaction in another system, all while respecting defined permissions.

The key difference lies in the level of autonomy: the person sets the goal and boundaries, while the agent navigates part of the process.

Automation: Rule-driven processes

Automation is the oldest concept on this list and doesn’t necessarily rely on AI. It enables the programming of a process to perform a specific action when a condition is met.

For instance, when a user fills out a form, the system can automatically send an email, following a predefined logic: when X happens, do Y.

When combined with AI, the technology can interpret information in one step and trigger an automated flow in the next.

Fine-tuning: Adapting AI to specific tasks

Fine-tuning involves retraining an AI using a specific set of examples to improve its performance on a particular task.

This process can be used to adapt a tool to a specific response style, format, or content type.

The aim is to modify the AI’s behavior to meet specific needs, rather than relying solely on instructions provided during each interaction.

Orchestration: Coordinating multiple tools in a workflow

Orchestration is the layer responsible for organizing the various components of an AI application, determining which tool to activate first, when another should join the process, and how information is transferred between steps.

A workflow can combine an agent, a document repository, an analysis tool, and an automation, with orchestration ensuring these components work together seamlessly.

For AI professionals, understanding these terms doesn’t require mastering the underlying engineering. The real benefit is being able to distinguish between tools that simply generate content and those that query data, execute tasks, or coordinate multiple steps, enabling more informed choices about a solution’s capabilities.