AI Essentials for CX Teams

Have you ever wondered how an AI agent knows what to say or why it sometimes gets it wrong? At the heart of today’s AI systems are LLMs, and understanding how they work is the key to designing and managing effective AI agents in customer support.

An LLM can be described as a prediction engine. It’s trained on billions of sentences to do one thing really well: predict the next word in a sequence. The model has absorbed vast patterns of human language, so it can generate coherent, original responses in real time and not just retrieve canned replies. This is why we call it generative AI.

LLMs are especially good at handling structured tasks with ambiguous input, exactly what customer support requires. They can summarize policies, draft step-by-step guides, or rephrase responses, while still interpreting messy, human language full of slang, typos, or multi-part questions.

But there are limits. Models only see what’s in their context window, the input you provide plus any generated text, measured in tokens (small chunks of text). If your grounding data or customer history is too long, the model may cut off key details, leading to unreliable answers.

This is why prompt design and orchestration matter so much. By shaping what the model sees, you can guide its focus and improve consistency. And since LLMs don’t come with your internal knowledge by default, techniques like retrieval-augmented generation (RAG) ensure that relevant help articles, policies, or past conversations are retrieved and added to the prompt before the model responds.

In summary, an LLM generates new responses word by word, making it well-suited for the structured yet ambiguous questions common in customer support. But its usefulness depends on how you design prompts, manage context, and ground the model.