10 Essential Tips for Using LLMs with Rich Content Messages

Rich content messages and Large Language Models (LLMs) seem like opposites at first glance. One offers structured, predefined options to guide users along a happy path. The other allows for freeform input, interpreting natural language in almost any way. So they are both very powerful tools to use in customer communications. Luckily you don't have to choose. Combining Large Language Models with rich content messages creates a powerful, flexible user experience—but only if done right. This post has ten essential tips for you to ensure seamless integration.

What Are Rich Content Messages and Why Do They Matter?

Rich content messages are interactive message formats designed to make user interactions more efficient and engaging. Instead of sending plain text, businesses can use structured elements like list messages, quick replies, and interactive cards to guide users toward specific actions.

• List messages present users with a set of predefined options, making them ideal for structured choices like selecting a product category, choosing an appointment time, or browsing FAQs.
• Quick replies offer one-tap responses that help streamline conversations by keeping users on a predefined path—perfect for confirming a booking, selecting a language, or answering yes/no questions.
• Interactive cards combine images, text, and buttons, making them useful for showcasing products, event details, or personalized recommendations in a visually compelling way.

A WhatsApp conversation that shows three messages of different rich content types

These message types are widely used across communication channels like WhatsApp Business Messaging and RCS Business Messaging (RBM), allowing businesses to provide clear, actionable choices while reducing the chances of user confusion. By structuring responses, rich content messages improve response times, minimize errors, and enhance the overall user experience. However, while they create a smooth, guided experience, they also have limitations — particularly when users need more flexibility.

How LLMs Unlock Flexibility in Conversations

While rich content messages excel at providing structure, they can sometimes feel restrictive. Customers don’t always fit neatly into predefined options — they might have specific preferences, ask follow-up questions, or phrase requests in unexpected ways. This is where Large Language Models come in.

LLMs allow users to express themselves naturally, handling freeform input in multiple languages while still understanding intent. Instead of forcing users down a rigid path, AI-powered chat interfaces can interpret vague or complex requests and respond intelligently. For example, rather than selecting “Cappuccino” from a menu, a user might type, “I want a strong but slightly sweet coffee, and I can’t have dairy.” A well-integrated LLM doesn’t just understand this—it can convert the request into a structured order and even personalize recommendations based on previous interactions.

Using a list template to suggest the AI-based recommendations

Tips for Using LLMs with Rich Content Messages

By blending LLMs with rich content messages, businesses can offer the best of both worlds: structured guidance for quick decisions and open-ended flexibility when users need it. But to make this combination work smoothly, there are a few key considerations to keep in mind.

1. Mind Template Limits

Rich content messages usually have strict format limitations, and exceeding them can prevent messages from being sent. For example, the subtitle of a twilio/card cannot be longer than 60 characters and cannot contain variables. AI-generated text doesn’t always respect length constraints, so don’t rely solely on the model to self-regulate. Instead, set clear length limits in your prompts and apply additional checks to ensure messages stay within the allowed character count.

2. Keep Templates Flexible

The more adaptable your templates are, the better your AI can work with them. Use placeholders instead of hardcoded text whenever possible, giving the AI room to customize responses dynamically. If the number of placeholders is fixed/limited, consider creating multiple templates for different scenarios (e.g., separate templates for three, five, or nine list items) and selecting the best fit at runtime.

3. Handle Translations Properly

LLMs can generate text in almost any language, but your templates may contain static text elements—like button labels or section headers—that don’t automatically adapt. Review your rich content messages to identify hardcoded elements and decide whether they should be translated.

Screenshot showing a hard-coded string that is writing in German while the rest of the conversation takes place in English

4. Use AI for Classification

One of an LLM’s greatest strengths is understanding unstructured user input and converting it into structured data. Leverage this capability by letting the AI categorize responses and map them to predefined options. For example, in a beverage-ordering scenario, the model can recognize that “I’d like a large oat milk latte with caramel” corresponds to an “order item” with “modifiers.” This allows users to be expressive while still interacting with a structured system.

5. Guide AI with Clear Prompts

LLMs interpret instructions flexibly, which can sometimes lead to unpredictable outputs. Be explicit in your prompts about the desired response format and structure. If your system expects a list, specify that the output should be in a list format. Using schema definitions or structured output techniques can further reduce ambiguity.

6. Sanitize AI Output

Never assume that AI-generated content is always valid or well-formed. Treat LLMs as untrusted clients and apply the same input validation techniques you would for human users. Ensure responses match the expected structure, check for missing or malformed data, and implement safeguards against potential hallucinations or inappropriate content.

7. Prioritize Readability

AI-generated responses should be concise, clear, and easy to scan. Long-winded explanations or overly complex wording can frustrate users, especially in quick interactions like rich content messages. Encourage the AI to keep responses short and structured, and limit the number of options to prevent decision fatigue.

8. Fallback Gracefully

Even with careful design, errors can happen—whether due to API issues, template mismatches, or unexpected AI behavior. If a rich content message fails to send, have a fallback in place. This could mean truncating the text, reformatting the message as one or multiple standard text responses, or prompting the user to refine their input.

9. Account for Platform Differences

Rich content messages may look and behave differently depending on the messaging platform. While Twilio’s Content Templates help standardize experiences across channels, individual messaging apps (like WhatsApp, RCS, or Facebook Messenger) may still have variations in how they display lists, cards, or carousels. Always test AI-generated content on the platforms you support to catch inconsistencies before they affect users.

Two slightly different renderings of the same template message on different apps. The left one shows the card in the native RCS messaging application (video plays in preview window and the text between the asterisk is not rendered bold. The right screen is from WhatsApp that only shows the video thumbnail and renders the text bold.

10. Use AI for Personalization

Personalized AI interactions are becoming the standard, with most businesses prioritizing tailored chatbot experiences. LLMs can dynamically adjust tone, content, and recommendations based on user preferences. For example, if a customer previously mentioned a lactose intolerance, the AI can filter out dairy-based drink options from future suggestions. Integrating customer data platforms like Twilio Segment can further enhance personalization, creating richer and more relevant interactions.

The AI makes personalized recommendations based on the custom traits of this particular customer.

Mastering the Blend: LLMs and Rich Content Messages

Bringing together the structured guidance of rich content messages with the flexibility of LLMs creates a best-in-class user experience—but only when done thoughtfully. By following these ten tips, you can ensure that AI-generated responses fit seamlessly into predefined templates, maintain clarity, and adapt dynamically to user preferences.

As conversational AI continues to evolve, the ability to balance structure and creativity will define the most effective chat interactions. Whether you’re guiding users through simple choices or handling complex, freeform requests, combining LLMs with rich content messages lets you deliver both efficiency and personalization. With the right implementation, you can create chat experiences that feel intuitive, intelligent, and truly user-friendly.

We can’t wait to see what you build!