There’s no shortage of AI buzzwords floating around, especially in customer experience. With every new product demo, funding announcement, or tool integration, the language gets murkier.

If you’re a CX leader trying to make smart choices, rally your team, and avoid being dazzled by the wrong things, you need clarity. 

This AI glossary clears up six of the biggest points of confusion in the AI + CX space and gives you the language to lead, not just follow.

For the full list of terms, visit the PartnerHero Glossary.

The big, confusing AI terms, decoded

Each of these terms is often used incorrectly—or used interchangeably with something else entirely. Here's what they really mean and why you should care.

1. Augmented AI

• What people think it means: Just another word for automation or AI with a human backup.
  
  
• What it actually means: AI tools that are intentionally designed to work with human expertise, not replace it—focused on collaboration, not substitution.
  
  
• Why it matters: It’s the foundation of most ethical, efficient, and empathy-driven AI in customer experience today.
  
  
• See definition in full glossary
  
  

2. LLM (Large Language Model)

• What people think it means: Any chatbot or AI tool = an LLM.
  
  
• What it actually means: A specific type of AI model trained on massive datasets to understand and generate language. Not all AI tools use LLMs, and not all LLMs are a fit for CX.
  
  
• Why it matters: Knowing the difference helps you ask the right questions when evaluating vendors or tools.
  
  
• See definition in full glossary
  
  

3. Agent Assist / Co-pilot

• What people think it means: Just a fancy chat widget or scripting tool.
  
  
• What it actually means: A tool that assists agents performing customer service by offering suggestions, information retrieval, or summarization—working alongside them, not replacing them.
  
  
• Why it matters: Crucial for increasing efficiency without losing the human touch.
  
  
• See definition in full glossary

4. Fine-tuning vs. Prompt Engineering

• What people think it means: They’re interchangeable terms.
  
  
• What it actually means:
  
  
  • Prompt engineering is guiding the model using input text.
    
    
  • Fine-tuning is retraining the model on custom data.
    
    
• Why it matters: Each approach affects cost, flexibility, and performance differently. CX teams need to know when to use what.
  
  
• See definition in full glossary
  
  

5. Human in the loop (HITL)

• What people think it means: A person is always watching what the AI is doing.
  
  
• What it actually means: Strategic checkpoints where humans guide, correct, or review AI outputs, especially in high-stakes or nuanced environments like support.
  
  
• Why it matters: Builds trust, improves outcomes, and keeps the AI system accountable.
  
  
• See definition in full glossary
  
  

6. Agentic AI

• What people think it means: A smarter bot or one that feels more human.
  
  
• What it actually means: AI systems that can act independently, make decisions, and carry out tasks with minimal human input, sometimes even initiating actions on their own.
  
  
• Why it matters: This is where AI starts to handle more sophisticated tasks based on reasoning. In CX, that could mean resolving more nuanced customer service issues—but it also raises questions about guardrails, escalation paths, and trust.
  
  
• See definition in full glossary

Final thoughts: language shapes leadership

Understanding these terms isn’t just for your AI team—it’s how CX leaders make smart decisions, guide their teams, and ensure ethical implementation.

If you’ve ever nodded through a meeting while secretly Googling acronyms, you’re not alone. Let’s demystify the jargon and focus on what actually works.

Want to explore more terms related to AI, CX, and more? Check out the full PartnerHero Glossary here.