What are chatbots and how do they work?

Chatbots are digital assistants that enable automated interaction with users—via text or voice. They answer questions, assist with navigating offerings, and can even handle complete business processes. What just a few years ago was considered futuristic is now an integral part of modern customer communication.

The term “chatbot” combines chat (conversation) and bot (short for robot), literally describing a robot capable of conversing with humans.

In an era when companies must be available around the clock and customers expect fast, precise answers, chatbots play a central role. They relieve pressure on service teams, speed up processes, and boost customer satisfaction—all while reducing costs. Especially in conjunction with artificial intelligence, chatbots unlock potential far beyond simple FAQ responses.

As a provider of conversational AI solutions with a focus on semantic technologies and knowledge graphs, Onlim specializes in developing powerful, scalable, and AI-supported chatbots. But to understand how modern chatbots work, it’s worth first looking back at their origins.

The idea of enabling machines to communicate with humans stretches back decades. In 1950, British mathematician Alan Turing introduced the concept now known as the Turing Test—a machine is deemed intelligent if it cannot be distinguished from a human in a written dialogue.

The first milestone was ELIZA, developed by MIT computer scientist Joseph Weizenbaum in 1966. ELIZA simulated a psychotherapeutic dialogue, responding to user inputs with simple text-pattern recognition—not real language comprehension. Nonetheless, it was a pioneer; for the first time, conversational exchange with a machine felt human-like. 

In 1972, PARRY, created by psychiatrist Kenneth Colby, took things further by simulating the thought patterns of a paranoid patient—a kind of “ELIZA with attitude.” It even managed to fool psychiatrists about half the time in a variant of the Turing Test. 

The chatbot landscape remained rules‑based through the 80s and 90s, with systems like ALICE (1995) employing AIML (Artificial Intelligence Markup Language) to respond based on scripted patterns. 

A breakthrough came with the rise of AI and machine learning: IBM Watson (2011) handled complex queries—famously winning on Jeopardy!. The advent of neural networks and powerful computing gave rise to systems that not only process language but interpret context.

Since around 2018, Large Language Models (LLMs) such as PaLM 2, Llama 2, and GPT‑4 have reshaped the chatbot landscape. These models—trained on billions of parameters—understand language patterns, semantics, and context deeply, delivering almost human-level responses.

Today, chatbots have evolved far beyond simple tools—they are digital assistants deployed in customer service, e-commerce, HR, public sector, and more. But what technologies actually power them?

Not all chatbots operate the same way. Broadly, there are two main types: rule-based chatbots and AI-powered chatbots. Each employs a distinct approach to user interaction, offering unique advantages and limitations.

For a chatbot to communicate meaningfully with humans, more is needed than just predefined text blocks. Modern chatbots are built on a multitude of technological components working together to understand speech, interpret it, and generate appropriate responses. At the core of this are technologies from the field of artificial intelligence, particularly Natural Language Processing (NLP).

NLP is a subfield of AI focused on the automated processing and analysis of natural language. It enables chatbots to interpret human language in ways that go beyond simple keyword recognition. It involves several subprocesses:

• Tokenization: Splitting text into individual words or phrases (tokens).
  
• Lemmatization/Stemming: Reducing words to their base form (e.g., “went” → “go”).
  
• Part-of-Speech Tagging: Identifying parts of speech (e.g., noun, verb).
  
• Entity Recognition: Detecting named entities such as names, dates, and places.
  
• Intent Recognition: Determining the user’s intent (e.g., “I want to book a hotel” → intent: “hotel booking”).
  

These processes lay the groundwork for the next level: true language understanding.

NLU is a subset of NLP that focuses on grasping the semantic meaning of language. The goal is not just to identify what the user says, but what they actually mean. This includes:

• Context Understanding: The chatbot takes previous messages or usage context into account.
  
• Ambiguity Resolution: Detecting ambiguous terms and selecting the correct interpretation.
  
• Error Tolerance: Handling typos, colloquial speech, or incomplete sentences.
  

NLU is crucial for a natural and fluid user experience and distinguishes intelligent chatbots from simple query systems.

Once a chatbot has understood the input, it needs to produce an appropriate response. This is enabled by Natural Language Generation. NLG is the process of turning structured information (e.g., a search result or database response) into coherent, conversational language.

A simple example:

• Structured Data: Product name = “coffee machine”, price = “79 €”
  
• NLG Output: “Our coffee machine is currently available for €79.”
  

Modern AI models like GPT‑4 excel at NLG, generating context-aware, dynamic, and even creative replies. Onlim combines rule-based generation with semantic NLG powered by enterprise knowledge graphs—for consistent and accurate answers.

While rule-based systems rely on fixed rules, AI-powered chatbots learn from data. Machine Learning (ML) allows chatbots to continuously improve their language understanding and response capabilities—based on real user interactions and feedback.

Key aspects:

• Training Data: Collection of typical user queries to identify intents and entities.
  
• Supervised Learning: Human-annotated examples to optimize the model.
  
• Feedback Loops: Using user feedback for ongoing improvements.
  
• Transfer Learning: Leveraging pretrained models like GPT or BERT, adapted to specific use cases.
  

With proper training, a chatbot becomes not only more accurate but also more adaptable which is an essential success factor for productive deployment.

Behind a functional chatbot lies more than just a chat window. Effective interaction requires a well-thought-out technological architecture—comprising multiple layers each handling different tasks. A modern chatbot is thus not a single program, but a complex interplay of various components.

An intelligent chatbot isn’t a finished product you build once and then leave alone

On the contrary: behind every successful chatbot lies a continuous learning process: from initial training to ongoing optimization in daily use. Especially for AI-based chatbots, language understanding and response quality depend heavily on how well the system is trained and how well it is maintained over time.

The foundation is a carefully curated dataset

This includes typical user inputs—known as utterances—which are mapped to specific intentions (intents). For example, the chatbot learns that phrases like “I need a hotel in Vienna” or “Can I stay overnight in Salzburg?” both indicate the intent “book hotel.” These datasets are enriched with relevant details called entities: for instance, place names, dates, or product names. The more diverse and realistic the data, the better the chatbot can handle novel phrasing.

During training, various machine-learning methods are applied

The most widespread is supervised learning, where the bot is trained with manually structured example sentences. Additionally, techniques like unsupervised learning or reinforcement learning can be used, e.g., to discover new topic clusters or to learn gradually from user feedback. In practice, many systems—including Onlim—use transfer learning. This adapts large pretrained language models like GPT or BERT to specific enterprise use cases, significantly reducing development time and improving performance.

Before a chatbot is launched, it undergoes comprehensive testing

This ensures that all intended intents are reliably recognized, the dialog flow makes sense, and the bot handles unclear or faulty inputs well. Automated tests are complemented by real users testing the system, and often A/B tests compare different response versions.

But learning doesn’t end at launch. A modern chatbot continuously collects data from real conversations, always GDPR-compliant, and analyzes it. Metrics such as intent recognition rate, drop-off rate, or successful conversation completions are tracked. These metrics help identify areas for improvement: Perhaps a frequently asked intent is missing? Or is the answer is unclearly worded? Or users consistently drop off at a certain point?

Onlim provides companies with a powerful training and monitoring platform that can be used even without deep technical expertise. This enables businesses to continuously enhance and evolve their chatbot—based on real data and within a structured optimization process.

In short: a chatbot is never “finished.”

Long-term success relies heavily on structured training, regular testing, and ongoing optimization. Over time, this transforms a digital assistant into a genuine conversational partner.

Many chatbots hit their limits when users ask complex or ambiguous questions. They often lack deep language comprehension, responding only to keywords or following rigid rules. Onlim goes a significant step further by combining AI with semantic technologies and knowledge graphs to enable true understanding.

Semantics means that the chatbot doesn’t just recognize what was said, but what was meant. This requires foundational knowledge of terms, meanings, and relationships—exactly what knowledge graphs provide. They structure knowledge as entities (e.g., “location,” “hotel,” “breakfast”) and their relationships (“is located in,” “includes,” etc.). For instance, the bot can understand that “Tyrol” is a region encompassing specific places and that “breakfast” can be part of a hotel offering.

Onlim merges this semantic knowledge structure with advanced Natural Language Understanding. The result: the bot can flexibly handle varied request formulations, without needing individual training for each variant, such as “I’m looking for a room with breakfast in Tyrol” or “Are there apartments as well?”

Typical use cases include product search with filters, event queries, or internal knowledge retrieval. The information is not just retrievable but logically connected and understandable to the bot.

With this hybrid approach, Onlim sets new standards: semantic intelligence isn’t an add‑on—it’s a core part of the chatbot architecture, crucial for precise, context-aware, and scalable communication.

Modern chatbots have evolved far beyond mere digital FAQ tools. Today, they serve as powerful automation engines: handling customer service tickets, generating qualified leads, booking appointments, and even empowering internal teams with streamlined workflows. Thanks to advanced semantic technology and knowledge graphs—as implemented by Onlim—these systems don’t just parse keywords. They understand complex, context-rich questions, deliver precise answers, and gracefully adapt across channels such as web chat, mobile apps, voice assistants, and messaging platforms.

But the real leap forward lies in the emergence of AI agents. These aren’t just reactive chatbots that respond when prompted; rather, they act with autonomy. Equipped with goal-driven reasoning, planning, memory, and action capabilities, AI agents can pursue objectives independently—whether initiating a purchase, updating systems, arranging complex multi‑step workflows, or orchestrating coordinated processes . They interpret intent, learn from interactions, resolve ambiguity, and trigger actions like opening tickets, scheduling meetings, or even executing transactions in backend systems .

This shift transforms chatbots into fully-fledged digital assistants with operational capabilities, not just conversational ones. The industry is rapidly embracing this transition: many platforms are expanding from static bots to agentic AI ecosystems. For example, enterprise agents can autonomously create support tickets, coordinate flight and hotel bookings, handle refunds, or guide internal teams, functioning almost like digital employees.

In conclusion, conversational AI is undergoing a fundamental metamorphosis: from reactive Q&A systems to proactive, intelligent, action-oriented assistants. Organizations that invest in the right combination of semantic intelligence, knowledge graphs, and agentic architectures gain both richer customer experiences and highly efficient operations.

Sources:

Wie funktionieren Chatbots? | Technikum Wien Academy

Was ist NLP (Natural Language Processing)? | IBM

Was ist Natural Language Processing (NLP)? · [mit Video]

What is a chatbot? + How they work

Geschichte der Chatbots – wie alles mit Eliza, Parry und Co. anfing. | melibo Newsroom

IBM Watson

Was sind Large Language Models? Und was ist bei der Nutzung von KI-Sprachmodellen zu beachten? – Blog des Fraunhofer IESE

Was sind AI Agents? | IBM