Can LLM be a new Scripting language?
Written by
Veena Nair
Post date
1 July 2024
Traditionally, automated conversations in customer service were driven by scripted bots that followed predefined paths.
These scripts were coded by developers, and Natural Language Processing (NLP) or Natural Language Understanding (NLU) engines were employed to decipher user input and navigate the conversation flow. However, this method often encountered limitations, such as losing the conversation path due to unexpected user responses or complex queries.
Today, with the advent of Large Language Models (LLMs), a new era of scripting for automated conversations is emerging.
When we upgraded our contact centres, we were all afraid to touch the IVR which had 1000s of scripted paths and complex set of routing rules.
Head of contact centre at a prominent bank in the UKTraditional scripted bots rely on fixed decision trees and predefined responses. While effective for simple, predictable interactions, they face several challenges:
Fixed scripts cannot adapt to unexpected or complex user inputs, leading to frustrating dead-ends. Updating and maintaining scripts requires significant manual effort, as any change must be coded and tested. As the complexity of interactions increases, scripting becomes exponentially more complex and difficult to manage.
Instead of following a rigid script, an LLM can understand the nuance of a user’s query and generate a contextually appropriate response on the fly.
LLMs maintain context throughout the conversation, allowing for more coherent and relevant interactions.
Implementing LLMs as a Scripting Language
We had observers go into the workplace and we timed people’s activities to the second. We’ve been to various workplaces, all high-tech companies. We wanted to look at information workers. We had observers shadow each person for three and a half days each and timed every activity to the second.
- Use APIs to connect the LLM to your CRM, ticketing systems, and other tools.
- Fine-tune the LLM on your specific business data to improve its understanding of your products.
- Where possible, combine the flexibility of LLMs with the reliability of rule-based systems.
Using LLMs as a new scripting language in automated conversations represents a significant leap forward in customer service technology. By leveraging the dynamic, context-aware capabilities of LLMs, businesses can create more engaging, efficient, and scalable customer interactions.
LLMs can handle a large volume of interactions simultaneously, making it easier to scale customer service operations as your business grows.