Certification and Training

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

Great post, Rodrigo. For me, RAG was probably one of the more confusing ones at first, mainly because it sounds very technical until you see the practical value of grounding AI responses in trusted knowledge rather than letting the model rely only on its own reasoning.

LAM was another one that took me a little while to fully connect, especially when the conversation shifts from just understanding language to actually taking action from it.

Really helpful way to break it down in a CX context.

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

Great post, Rodrigo. For me, RAG was probably one of the more confusing ones at first, mainly because it sounds very technical until you see the practical value of grounding AI responses in trusted knowledge rather than letting the model rely only on its own reasoning.

LAM was another one that took me a little while to fully connect, especially when the conversation shifts from just understanding language to actually taking action from it.

Really helpful way to break it down in a CX context.

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

Excellent breakdown. I really liked the practical approach.

One thing that helped me was realizing that these terms become much easier to understand when you map them to a real CX journey:
ASR hears,
NLU understands,
LLM generates,
RAG grounds,
Copilot assists,
and automation helps execute the next step.

That makes the conversation much less abstract and much more operational.

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

Great delivery, as usual, Rodrigo. As others have mentioned, I appreciate the practicality of your post and application within Genesys Cloud. There's something for everyone in your alphabet gumbo, haha

I explained one of these concepts earlier this week to a team member (before I read this), so your timeliness is great. Preparing to share this post with my entire team now 🤜🤛

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?

If you've been in CX for a while and recently started hearing more about AI, you've probably noticed something: the conversations suddenly sound like alphabet soup. AI this, LLM that, RAG here, LAM there … and everyone nods like it's obvious.

When I first started digging deeper into AI in the CX space, I realized that understanding the acronyms is half the battle. So here's a quick, practical guide to some of the most common ones - what they mean, and what they actually look like inside a Genesys Cloud world.

AI (Artificial Intelligence)
This is the umbrella. In CX, AI is what allows systems to automate, predict, assist, and personalize interactions.
In Genesys Cloud, AI shows up everywhere - from routing decisions to virtual agents to agent copilots.

NLU (Natural Language Understanding)
This is the part of AI that understands what someone means, not just what they say.
In Genesys Cloud, NLU powers bots so they can interpret customer intent instead of relying on rigid menu options.

LLM (Large Language Model)
This is what made AI feel "human" overnight. LLMs can generate, summarize, and respond in natural language.
In Genesys Cloud, LLMs can support things like summarizing conversations, assisting agents, or generating responses in real time.

RAG (Retrieval-Augmented Generation)
One of the most important (and underrated) concepts right now. RAG combines LLMs with real, trusted data sources.
Instead of "guessing," the AI retrieves information (like your knowledge base) and uses it to generate accurate responses.
In Genesys Cloud, this is key for grounding AI in your company's actual content and policies.

LAM (Language Action Model)
Think of this as the bridge between understanding and doing. It not only understands language but can take action based on it.
In a Genesys Cloud context, this could mean triggering workflows, updating records, or executing tasks based on a conversation.

AVA (Automated Virtual Agent)
This is your AI-powered bot - voice or digital - handling interactions without a human agent.
In Genesys Cloud, AVAs are what engage customers first, resolve simple requests, and escalate when needed.

I have recently done a webinar on this topic, you can find it here: https://education.genesys.com/explore/course/18389

Bonus ones you'll hear a lot:

CX (Customer Experience)
Not new, but worth grounding. CX is the sum of every interaction a customer has with your brand.
AI is not replacing CX - it's scaling it. In Genesys Cloud, it helps deliver faster, more consistent, and more contextual experiences.

ASR (Automatic Speech Recognition)
Turns speech into text. It's what allows voice bots to "hear" customers.

TTS (Text-to-Speech)
Turns text into natural-sounding speech. It's what gives voice to your virtual agents.

Copilot (Agent Assist)
AI that works alongside your agents - suggesting responses, summarizing conversations, and reducing effort.
In Genesys Cloud, this is where AI becomes a real-time partner, not just automation.

The interesting shift isn't just that these technologies exist, it's how they're coming together.

We're moving from isolated capabilities (a bot here, a routing rule there) to connected AI, orchestrated systems, that understand, decide, and act across the entire customer journey.

And the more you understand the language behind it, the easier it becomes to see where it actually drives value.

Curious to hear from others: which of these terms felt the most confusing when you first started exploring AI in CX?