How LLMs Interact with the World

Large Language Models (LLMs) are powerful, but on their own, they have limited functionality. They don’t browse the internet, retrieve real-time data, or take actions—they only generate text.

So how do businesses make AI useful in the real world? The answer lies in APIs, embeddings, and plugins, which help LLMs interact with external data, tools, and workflows.

But before we dive in, I have to reiterate:

👉 LLMs can only operate on text.
👉 Text in, text out.

Anything beyond that—like retrieving external data or taking action—is a bit of a magic trick performed by AI developers, who design ways for AI to communicate with software tools and systems.

LLMs are just text processors—but when connected to APIs, embeddings, and plugins, they can integrate with external systems and perform real business functions.

Key Takeaways for Executives:

✅ LLMs don’t “know” anything beyond their training—they need APIs for real-time information.
✅ Embeddings allow LLMs to “recall” custom knowledge, like company data.
✅ Plugins and tools let LLMs trigger actions—like booking a meeting or running a report.
✅ The real power of AI comes from connecting it to business data and workflows.

Instead of asking “What can AI do?”, businesses should ask:
👉 “How can we integrate AI with our tools and data to unlock real value?”

1. APIs: Connecting LLMs to External Data

LLMs are not connected to the internet or real-time databases by default. They only generate text based on their training data.

What APIs Do:

APIs (Application Programming Interfaces) connect AI to external data sources, allowing it to:
✔️ Retrieve real-time financial data (e.g., stock prices, exchange rates).
✔️ Pull live customer data from CRM systems.
✔️ Fetch updated news, weather, or compliance data.

Example Use Case:

A chatbot without an API can only generate generalized answers.
A chatbot with an API can pull live product availability, customer details, or support tickets.

💡 APIs allow LLMs to act as a dynamic interface rather than a static knowledge generator.

2. Embeddings: Giving AI a “Memory”

By default, LLMs don’t remember anything outside their training data. However, embeddings allow AI to retrieve relevant information from custom business documents and databases.

What Embeddings Do:

Embeddings convert text into numerical representations, allowing AI to:
✔️ Recall company-specific knowledge (e.g., policies, product manuals, customer history).
✔️ Search large knowledge bases efficiently.
✔️ Provide contextual responses based on internal documentation.

Example Use Case:

Without embeddings: AI can’t answer company-specific questions.
With embeddings: AI can retrieve answers from internal reports, contracts, or customer FAQs.

💡 Embeddings let LLMs “look up” business data instead of relying only on general training data.

3. Plugins & Tools: Letting AI Take Action

The “Magic Trick” of AI Taking Action

Since LLMs can only process text, getting them to perform tasks (like booking meetings or fetching reports) is a trick of AI engineering.

Here’s how it works:
1️⃣ User input is sent to the AI (e.g., “Schedule a meeting for me at 3 PM.”).
2️⃣ The AI responds with text indicating an action (e.g., “Use the scheduling tool to book this meeting.”).
3️⃣ The AI developer writes code that interprets this response and runs an external function (e.g., actually scheduling the meeting via Google Calendar).
4️⃣ The results are fed back to the AI as more text (e.g., “The meeting has been scheduled successfully.”).

💡 AI itself is still just generating text—developers create the surrounding system that turns those responses into real actions.

What Plugins & Tools Do:

Plugins enable LLMs to execute real-world tasks, such as:
✔️ Booking meetings via a calendar system.
✔️ Placing e-commerce orders.
✔️ Running SQL queries to fetch reports.

Example Use Case:

Without plugins: AI can recommend how to schedule a meeting.
With plugins: AI can actually schedule the meeting in Google Calendar.

💡 Plugins make AI truly useful by enabling action-taking, not just text generation.

The Business Case for AI Integration

For executives, the key takeaway is: LLMs alone are not enough—real business value comes from integration.

How Businesses Leverage LLM Integration:

✔️ Customer Support AI → Uses APIs to pull customer data & resolve tickets.
✔️ AI-Powered Search → Uses embeddings to retrieve company-specific answers.
✔️ AI-Driven Automation → Uses plugins to trigger actions like scheduling, ordering, or reporting.

🔹 Executives should focus on how AI connects to business workflows rather than just using standalone AI models.

Final Thoughts

LLMs are powerful but limited on their own. The real value comes from APIs, embeddings, and plugins, which enable AI to:
✅ Access real-time data
✅ Retrieve custom business knowledge
✅ Execute real-world actions

👉 Instead of seeing AI as just a chatbot, businesses should integrate AI into their existing tools and workflows to drive real efficiency and automation.