Conversational AI
AI is a hot topic, but many people don’t know much about it. Here’s a cheat sheet for the next time someone brings up AI in conversation.
AI Terms and Acronyms You Actually Need to Know
AI — Artificial Intelligence — Software that can learn, reason, and make decisions — like a digital brain that gets smarter over time.
Agent (so hot right now…) — An AI That Takes Action — Most AI tools answer questions. An AI Agent goes further; it can make decisions, use tools, and complete multi-step tasks on its own. Instead of just telling you how to send a follow-up email, an Agent can actually send it for you.
API — Application Programming Interface — The bridge that lets two apps talk to each other. When your CRM automatically gets a new contact from your website form, an API made that happen.
Automation vs. Augmentation — Automation replaces a task entirely. Augmentation makes a person better at doing their job. Both have a place, and knowing the difference helps you use AI tools effectively.
Context — The information you give an AI before asking it to do something. The more relevant context it has, the better the result.
Fine-tuning — Training an existing AI model on your specific data so it becomes more useful for your particular business or industry.
GPT — Generative Pre-trained Transformer — The technical name for the model behind ChatGPT. "Generative" means it creates new content. "Pre-trained" means it learned from billions of documents before you ever used it.
Hallucination — When an AI states something confidently that isn't true. Not a glitch, just how the models work. Always verify outputs before acting on them.
Integration — Connecting two or more software tools so they share data and trigger actions automatically. When your form submission creates a contact in your CRM, that's an integration.
LLM — Large Language Model — The technology behind tools like ChatGPT and Claude. It's trained on massive amounts of text and can read, write, and summarize like a human.
ML — Machine Learning — A type of AI that learns from examples instead of being programmed with rules. The more data it sees, the better it gets.
On-Prem (On-Premises) — Software or hardware that runs on your own machines, in your own location, instead of in a cloud data center somewhere else. It's the opposite of SaaS. You own it, you control it, and it doesn't require sending your data to a third-party server to function.
Prompt — The instruction you give an AI tool. The quality of what you get back is almost entirely determined by how well you write it.
RAG — Retrieval Augmented Generation — A way of giving an AI access to your specific documents or data so its answers are based on your information, not just general knowledge.
RPA — Robotic Process Automation — Software that mimics repetitive human tasks within your desktop — clicking buttons, filling forms, copying data — without a human touching it.
SaaS — Software as a Service — Any software you access through a browser and pay for monthly instead of installing. Gmail, HubSpot, and Slack are all SaaS tools.
Why All Models are Not Created Equal
Parameters — The building blocks of an AI model's intelligence. During training, a model processes enormous amounts of text and adjusts billions of tiny numerical values, called parameters, to learn how language works. The more parameters a model has, the more nuance it can capture and the broader its knowledge.
A model with a trillion parameters (1T) can handle complex reasoning across almost any topic, but it requires massive computing power to run, which is why the largest models live in data centers. A model with 7 billion parameters (7B) is far smaller, but when it's trained on focused, relevant data it can still be remarkably capable for specific tasks. Smaller models use less power, cost less to run, and can operate on hardware that already exists in homes and offices.
When someone says a model is "large" or "small," they're talking about parameter count. Currently, billions is common. Trillions is frontier territory.
Tokens & Inference — How AI Thinks on Demand
When you send a message to an AI model, it breaks your text into small chunks called tokens — roughly one token per word. It then runs those tokens through its model to generate a response. That process of generating a response in real time is called inference.
This is how AI tools charge based on usage. Every message you send and every response you get costs a certain number of tokens to produce.
Current pricing is per million tokens which is about 750,000 words; this changes frequently, so the numbers below are just to give you an idea:
ChatGPT (OpenAI)
Model Input Output
GPT-4o Mini $0.15 $0.60
CloudidrGPT-5.4 $2.50 varies
Claude (Anthropic)
Model Input Output
Haiku 4.5 $0.25 $1.25
Sonnet 4.6 $3.00 $15.00
Opus 4.6 $5.00 $25.00
Gemini (Google)
Model Input Output
2.5 Flash-Lite $0.10 $0.40
Cloudidr2.5 Pro $1.25 $10.00
From 2024 to 2026 these prices have dropped by around 80%, meaning it’s cheaper to use AI than it’s ever been.
Have questions about what this means for your business? I'd love to talk it through. Book a time on my Calendly, or reach me directly at sam@bright-pivot.com.
