Understanding LLMs Through Practical Use

Big Idea

The fastest way to understand large language models is not to study the theory first. It is to use them in practical workflows you already recognize.

This lesson shows how AI can help insurance professionals create, reuse, restructure, and capture work. The goal is not to learn tricks. The goal is to see how AI can become part of ordinary agency work.

Practical Use, Not Theory

Large language models can seem mysterious. It is natural to wonder how they understand questions, create content, and organize messy ideas. But for most agents, the more important question is not how the models are built. The more useful question is how they can be used in day-to-day work.

That shift matters. You do not need to become a technical expert before you begin using AI. You need to learn how to work with it in situations that already exist in your business.

In this lesson, AI is demonstrated through several practical workflows: creating FAQs, repurposing content, turning old work into new assets, cleaning up rambling ideas, capturing value, and developing targeted marketing.

Create Content from Scratch

The first workflow is simple: use AI to generate FAQs on a topic. Agents already answer client questions every day. The difference is that AI can help create a structured starting point quickly.

For example, if you are building an FAQ section about HSAs, AI can brainstorm the questions clients are likely to ask. It can suggest questions about eligibility, high deductible health plans, contribution limits, qualified expenses, tax advantages, and what happens if someone contributes when they are not eligible.

That does not mean you should copy and paste without review. You still need to verify the answer, especially when the topic involves current dollar limits, compliance details, or exceptions. AI is strongest with common patterns and weaker with edge cases. Your judgment still matters.

Once the answers are cleaned up, they can become part of your website, your client communication library, or your internal knowledge base.

Turn Output Into an Asset

The value of the FAQ demo is not just that AI can produce answers. The value is that the answers can be captured in a reusable system.

After generating FAQ answers, you can ask AI to format them for WorkFlowy. If the formatting is wrong, you can correct it. For example, if dashes do not paste properly, ask for indentation only. Once the result is in a clean code wrapper, it can be copied and pasted into your outline.

That turns a one-time AI response into a structured asset. The next time an HSA question comes up, you do not have to start over. You have a place to go, a reusable answer, and a system that grows over time.

Repurpose Content

The next workflow is repurposing. An FAQ can become a blog post. A blog post can become a newsletter. A newsletter can become a LinkedIn post. A CE class can become an article. AI makes that conversion much faster.

In the demo, a short FAQ about why HSAs are called triple tax advantaged was expanded into a blog post. Then the same topic was reshaped into a newsletter article. Then the audience was changed from consumers to agents.

This is the work-to-asset idea from earlier in the course. You do not need a brand-new idea every time. You need to take useful ideas and adapt them for different audiences, formats, and purposes.

Turn Old Work Into New Assets

AI can also help you reuse old work. An old article, set of notes, draft, or rejected idea may contain useful principles even if the original piece is not ready to publish.

One approach is to ask AI to pull out the atomic chunks. These are the complete standalone ideas underneath the larger piece. They are not just summaries. They are the reusable building blocks.

For example, an old article about procrastination might contain principles about why people delay simple tasks, how avoidance creates mental weight, and why starting is often the hardest part. Those ideas can be extracted, cleaned up, and recombined into something new.

This is one reason the knowledge base matters. When your old work is stored in reusable chunks, AI can help turn it into new articles, training materials, client explanations, and internal resources.

Rambling Prompts Are Allowed

You do not need to be a prompt engineer to get value from AI. For everyday thinking and writing, you can speak naturally, give context, and let the tool help structure the idea.

A messy dictated note can become an article. The article can become atomic chunks. The strongest chunks can become a new article. The key is iteration, not perfection.

Prompt precision matters more when you are building a tool that needs consistent output every time. But when you are brainstorming, drafting, or developing ideas, the first prompt can be rough. The conversation is what improves the result.

Use AI for Targeted Marketing

AI can also help turn knowledge into outreach. If you have a strategy that may fit certain clients, AI can help draft an email or guide that introduces the idea before renewal.

The lesson used two examples. One was an HSA email for clients who do not currently offer an HSA-qualified plan. The other was a marketing piece for churches, where the framing should lead with stewardship, care, and responsibility rather than simply leading with insurance.

This connects several earlier lessons. Strategies become more powerful when they are captured. Clients become assets when real examples and testimonials are collected. AI helps package those assets into useful communication.

Capture the Value

If useful AI output stays inside a chat, it can disappear just like a file buried in a folder or an email lost in an inbox. That creates a new version of the same retrieval problem.

The solution is to extract and store the good material. Ask AI to summarize what was agreed on. Ask it to put the result in WorkFlowy format. Copy it into your system. The goal is to make the output reusable.

AI can generate a lot of useful work quickly. Without a capture habit, much of that value is lost.

Work With It, Not Just Through It

The most important lesson from the demos is that you are working with AI, not simply commanding it. The first answer is a starting point. You respond, adjust, ask follow-up questions, and refine the result.

That back and forth is where the value often appears. AI can reflect your thoughts back to you, clarify them, and help you see connections you may have missed. Then you can capture the result and use it again.