How to Engage Customers With LLM-Powered Conversational AI

Rule-based chatbots and NLP-powered AI have been around for more than a decade. While they initially promised to be revolutionary, they ultimately fell short of their expectations. They could only handle rigidly structured scenarios or pre-identified intents when customer engagement is dynamic by nature.

However, the situation completely changed with the rise of Large Language Models (LLMs). These cutting-edge AI models can understand any instruction and craft relevant answers while adhering to the rules set in the instruction. Giving instructions to AI became an art of its own and even created a whole new industry of prompt engineering.   

For companies pursuing Product-Led Growth (PLG), nothing is more important than engaging with your customers scalably and effectively during the product life cycle. Traditionally, this has included strategies like a simple onboarding process, faster time to value, and easy-to-understand pricing. 

Now, with conversational AI, PLG can live up to its name. It can scalably touch every customer with human-like engagement during their life cycle as an AI chatbot and empower human support agents to serve more customers with fewer resources. AI acts as an additional brain and extra hands. 

Let’s uncover how LLMs gave birth to 3 new conversational AI strategies that can elevate customer engagement to a new level.

1. Give AI chatbots personas and instructions to follow

Creating an effective AI chatbot for your customers starts with clarity about its identity and role. It's pretty straightforward: describe the bot's role, such as a "customer support expert for a fintech company”, and it will adapt to that function, ready to meet your specific business needs. 

Of course, you would need further customization to contextualize the bot with your information (for instance, you could teach your bot to retrieve first-party data: “Retrieval”) to fit your exact requirements. By specifying its role and function, you can create an adept and natural AI chatbot.   

The ultimate strength of the approach is its flexibility. The bot's instructions can be dynamically changed or updated during conversations, ensuring it remains versatile and responsive. This adaptability means you're not limited to a single set of scenarios or functions.

For example, let’s assume you operate a recruiting platform that matches recruiters and applicants. You can create an AI recruiter that initially starts with an instruction to collect the applicant's personal information. Once complete, it can seamlessly transition to collecting details about their professional experiences. 

Magically, the AI determines the timing for these transitions, streamlining the process without manual oversight. So now, with LLMs, you are not just creating an AI chatbot but also one that can seamlessly handle abstract tasks without sticking to rigid scenarios in a way that can send customers running. 

Imagine the endless possibilities defining your AI chatbot as a salesperson, nutritionist, health coach, real estate agent, and on and on.     

2. Enhance AI chatbots with APIs and workflows

APIs have been one of the standard tools to augment previous-generation chatbots’ limited capabilities. While powerful, they could be leveraged only when customers followed exact scenarios, which required significant effort for companies to define. Despite the effort invested, they rarely made for good customer interactions. 

For instance, to cancel an order, customers had to follow detailed and intricate paths set by businesses. Users often found themselves at a dead-end and had to restart the conversation from scratch

LLM-powered conversations now flip this experience on its head, enabling customers to blaze their own paths. OpenAI’s latest function calls allow AI chatbots to detect intent and trigger the correct API call in real time. The chatbots can automatically fill in the necessary API call parameters with conversation information and generate a valuable answer with the data of the API’s response. 

This approach transformed a rigid and complex interaction into a nimble personalized experience that meets customer’s needs in real-time. Customers feel empowered instead of frustrated.

For instance, let’s say you want to cancel an order you made in an e-commerce store. Instead of browsing menus, you can just say, “I want to cancel my order,” and the chatbot will take the proper action. Also, LLM-powered chatbots now come with state-of-the-art intent classification and language understanding, meaning you don’t have to worry about how it will detect the correct phrase.

Another advantage of LLM-powered chatbots is that the legacy deterministic rule-based and AI approaches are mutually inclusive. Because the chatbots are context-aware, they will always be able to adapt to a customer following a predefined workflow. 

For example, let’s say that a customer is inquiring about a refund policy in a ride-hailing app. They may have to initially follow the menu path by navigating “Refund” > Select a specific ride > Select one of 5 common reasons for refund, but then the chatbot can take over with full context and provide the latest refund policy defined in the knowledge base. 

Ensure your AI chatbot supports a hybrid approach, including Workflows. This hybrid approach will help rein in the creative nature of generative LLM chatbots and reduce hallucinations and errors. This balancing act between the LLM and workflows will ensure the best responses while eliminating, reducing, or delaying human intervention.

3. Augmenting human support agents

Let’s shift gears to how conversational AI can empower every agent to be a super agent. While chatbots will continuously expand their scope of engagement with customers, human intervention is still necessary, and AI can help.

Common AI aids for customer service agents include summaries, autonomous response generation, recommended answers, and copy-editing with a specific tone and style. These functionalities help agents get the context quickly, enhance their answers, and operate faster. LLM’s universal abstraction of human languages also helps agents provide seamless multilingual support. 

At the end of the day, every business has slightly different needs, but AI is flexible enough to help most businesses. Whether you want to tag customer conversations, extract keywords, categorize conversations into topics, or analyze customer sentiment and rate responses, LLMs can enhance custom actions with a simple API request.  

Transform your customer experience with AI chatbots and AI-augmented features

Harnessing the power of conversations is a proven strategy to drive Product-Led Growth (PLG), and LLM-powered conversational AIs take it to the next level. AI chatbots empower businesses to create engaging, human-like conversations, excelling in tasks like assisting potential customers with recommendations, generating leads, and resolving customer inquiries. They supercharge agents to do more with less by augmenting their capabilities. 

To meet your application’s conversational AI needs, look for an AI chatbot service that can quickly get you started by simplifying the process through user-friendly, no-code interfaces and providing programmable customization capabilities to customize your automation needs.  

Sendbird, an all-in-one communications platform + AI for web and mobile apps, is worth exploring. Sendbird empowers businesses with intelligent AI chatbots and AI augmentations for customer support agents. To build your first custom GPT for your website or mobile app, try our free trial or contact us to discuss how to take your digital communication to the next level. 

Further reading:

Learn more about how AI can elevate CX

• Incorporate Conversational AI Chatbots Into Your Product-Led Strategy by Sangha Park

• Leverage AI for Customer Experience by Product School