Customer service AI: 10 more important definitions you need to know

Level: Beginner

Artificial Intelligence is not only evolving at an incredibly fast pace, it also “forces” us to learn specific terminology in order to use it confidently — and even more importantly, to understand which AI solution for customer service we actually need.

In this article, we’ll break down ten of the most popular terms so you can start getting familiar with this technology and what it can do.

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Machine Learning (ML)
• Deep Learning (DL)
• Retrieval-Augmented Generation (RAG)
• Sentiment analysis
• Copilots
• AI agents
• Hallucination

AI VOCABULARY FOR CUSTOMER SERVICE

1. NATURAL LANGUAGE PROCESSING (NLP)

Natural Language Processing is a branch of AI focused on teaching machines to understand, interpret and generate human language, both written and spoken.

Thanks to NLP, a system can read a text, recognise tone and respond appropriately — taking context into account and choosing the right words.

2. LARGE LANGUAGE MODELS (LLMs)

As part of NLP, Large Language Models are AI systems trained on vast amounts of text (books, articles, chats, websites, etc.) to enable machines to understand and communicate much like humans.

Customer service applications:

• Conversational chatbots
• Summarising calls or support tickets
• Auto-generating responses
• Agent copilots

4. MACHINE LEARNING (ML)

Machine learning is a method that enables machines to learn how to perform tasks by example.

Once trained with a few examples and told what to look for, the system can extract data and generate responses based on what it’s learnt.

5. DEEP LEARNING (DL)

Deep learning is a more advanced form of Machine Learning that also teaches systems through examples — but without needing humans to define rules or variables in advance.

It requires more data, but in return can recognise much more complex patterns with higher accuracy.

As the name suggests, it’s based on deep neural networks: structures made of interconnected layers that mimic how human neurons work.

Each layer processes and transforms the information before passing it to the next — building a deeper understanding with every step.

6. RETRIEVAL-AUGMENTED GENERATION (RAG)

Retrieval-Augmented Generation is a technique that combines LLMs with external data sources to allow the system to search for relevant information before crafting a response.

How does it work?

1. The user asks a question (e.g., “What’s our returns policy?”)
2. The system searches an internal knowledge base (like CRM documentation)
3. It extracts relevant snippets (e.g., “page 4 of the returns manual”)
4. The system feeds that info to the LLM
5. The LLM generates a response using that data as context

7. SENTIMENT ANALYSIS

Sentiment analysis is an AI capability that detects the emotional tone of a message — whether positive, negative or neutral.

AI models are trained on thousands of real-life examples labelled by sentiment. This helps them identify linguistic patterns that show whether someone is satisfied, angry, frustrated, and so on.

8. COPILOTS

Copilots are smart assistants embedded in your everyday work tools (like contact centre platforms, CRM, Word, email…) that interact with you in real time.

They can translate messages, write emails, summarise meetings, analyse documents — and more.

They rely on four key components:

• Large Language Models (LLMs): to understand instructions and generate content
• Natural Language Processing (NLP): to interpret user intent
• Retrieval-Augmented Generation (RAG): to search external data sources
• Integrations: to create tickets in a CRM, populate emails, etc.

9. AI AGENTS

AI agents are the next step after chatbots and voicebots. Unlike their predecessors, they’re powered entirely by generative AI, so they can respond and solve problems without following a fixed conversation flow.

They work thanks to five main components:

• Large Language Models (LLMs): for generating text, making decisions, and orchestrating actions
• Retrieval-Augmented Generation (RAG): to fetch external information
• Memory: to store data and past interaction history
• Tools: integrations that let them make changes in external systems
• Planning: to break down complex tasks into simpler steps

All this comes together seamlessly every time a user makes a request.

10. HALLUCINATION

When we say a bot “hallucinates”, we mean it generates a response that sounds right — but isn’t.

This happens when the model lacks context or relies on a poorly organised knowledge base.

How can we prevent it?

• Use RAG techniques to ensure responses are grounded in real, verified information
• Organise content by topic or workflow so the system retrieves the most relevant data
• Configure the bot to escalate to a human agent when unsure

FINAL THOUGHTS

For a bot to support your customers or assist your agents, it needs the right mix of capabilities:

• NLP and LLMs to read and interpret human language
• Machine Learning to improve with each interaction
• Deep Learning to uncover complex patterns in data
• RAG to access external knowledge sources

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