Why this matters now
AI is no longer something “for the tech team.” Whether you’re advising clients, closing contracts, or processing compliance reports, AI is already showing up in your software and service stack. But too often, conversations around AI are riddled with jargon.
If you’re tired of pretending to know what a “RAG pipeline” or “agentic system” is—this glossary is for you.
Let’s get you fluent in the language of modern AI. No PhD required.
Must-Know AI Terms, Translated
1. AI Model
An AI model is a trained system that can generate or interpret information. Think of it as a digital intern who’s read everything and can now answer questions, draft text, or sort data.
Used in: Email generation, chatbots, report summaries.
2. Large Language Model (LLM)
A type of AI model trained on billions of words. It powers tools like ChatGPT, Gemini, and Salesforce GPT. LLMs can draft emails, summarise contracts, and even write code.
Think: Google meets a junior analyst.
3. Prompt
The instruction you give an AI model. The better your prompt, the better the output.
Example:
✖️ “Help with report.”
✔️ “Summarise this month’s compliance report in bullet points using plain English.”
4. Prompt Engineering
The art of writing better prompts. It’s like learning how to brief a team member so they get it right the first time.
5. Agent
An AI agent doesn’t just answer questions—it takes actions. It can access tools, retrieve documents, and complete tasks across systems.
Example: An agent that drafts an onboarding email, pulls the right client file, and sends it for review—automatically.
6. Agentic AI
AI that actively works toward a goal, using reasoning and memory. It plans, adapts, and executes tasks, often with minimal human input.
7. Real-World Example: Agentic AI in Deployment
In a recent LinkedIn post by Salesforce, Baiju Devani describes how organisations are shifting from experimental AI pilots to full-blown agentic deployments. These are proactive systems that complete processes—not just provide suggestions.
“Agentic systems will increasingly handle full processes—not just answer questions.”
— Baiju Devani, Salesforce
Agentic AI represents the move from “AI as a helper” to “AI as a colleague.”
8. Autonomous Agent
A self-directed system that works through tasks based on goals and context. It can decide what step to take next, pull information from tools, and ask for help if it hits a blocker.
Used in: Client data collection, mortgage application pre-screens, follow-up email sequences.
9. Tool Use
An AI agent’s ability to interact with APIs or other software tools. This is what lets AI retrieve ABN details, update CRMs, or send Slack messages.
Your AI isn’t just chatting—it’s doing the work.
10. RAG (Retrieval-Augmented Generation)
RAG combines search + generation. Instead of relying just on what the AI remembers, it “looks up” facts in your docs or systems before answering.
Used in: Knowledge bases, contract review, policy queries.
11. Fine-Tuning
Customising an AI model to your business by training it on your data. That means answers that sound like your team, not generic Google search results.
Used in: Client Q&A, branded templates, document summaries.
12. Evals
Short for “evaluations.” These are structured tests to check how well an AI performs a specific task.
Think: Quality assurance for your model before it’s client-facing.
13. Chain of Thought (CoT)
A method that encourages the AI to reason step-by-step. This makes complex decisions more accurate and easier to explain.
Used in: Legal risk assessments, financial projections, multi-step logic tasks.
14. Zero-Shot & Few-Shot Learning
Zero-shot: The AI performs a task with no prior examples.
Few-shot: You give a few examples, and the AI learns from them.
Used in: Quickly adapting AI to new workflows—without writing code.
15. Hallucination
When the AI makes up facts. Often due to vague prompts or lack of context. (Yes, even ChatGPT can do it.)
Fix it by: Using retrieval (RAG), clearer prompts, and human checks.
What This Means for Financial, Legal & Property Firms
You don’t need to become a data scientist to lead AI change—but you do need to understand the fundamentals.
With this glossary, you can:
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Ask smarter questions in AI vendor meetings
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Brief internal teams with clarity
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Spot risks (like hallucinations or lack of evals)
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Push for practical use—not hype
How to Get Started
Use these terms to guide your AI implementation:
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Start with pilot tasks like summarising docs or generating client FAQs
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Use Gemini, ChatGPT, or Salesforce GPT but train them on your content
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Build agentic workflows for onboarding, AML/CTF checks, or marketing follow-ups
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Run evals before client-facing rollout
What Argo Logic Recommends
Argo Logic helps firms in financial, legal, and property services move from AI confusion to confident action. Here’s how we do it:
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🔍 AI Readiness Audits: Where to start, what to fix
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🛠 Prompt & Agent Design: Custom to your stack (Xero, Class, Salesforce)
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📊 Risk Management & Evals: So you stay compliant and secure
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🔁 Training & Handover: So your team owns the outcomes
👉 See how we help firms like yours
Takeaway Tips
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Think of prompts like briefing a junior analyst: clear, focused, with examples.
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Don’t be afraid of agentic AI—just test it before you trust it.
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Jargon-free teams adopt AI faster (and with less frustration).
Ready to Speak the Language of AI?
Book a free AI-readiness session with Argo Logic
👉 Click here to get started
