Mastering the Large Language Models(LLMs) Landscape: 5 Considerations for Customer Support Leaders
It took Facebook a total of 4.5 years to hit the 100 million users mark. WhatsApp and Instagram achieved that feat in 3.5 and 2.5 years respectively.
And now, a wunderkind large language model (LLM) has swept everyone off their feet in just five days—ChatGPT. Its ability to engage in meaningful conversations on a wide range of topics has captured the interest of engineers, customer support leaders, linguists, marketers, students, and more.
But is it ready to be a full-fledged part of the equation? If you’re a support leader, you must consider various factors to ensure that the LLMs meet the requirements and deliver results. They include:
One of the most critical considerations when deploying LLMs is security. LLMs are trained on large datasets that may contain sensitive information.
That makes it essential to deploy the model in a secure environment that is compliant with industry regulations and standards. Single-tenant solutions help address this concern by mitigating the risk of data leakage by isolating each instance from the others.
It is also important to implement appropriate measures such as encryption and access controls to prevent unauthorized access within the organization.
Hear what ChatGPT has to say about its security:
Are you a secure tool to use?
As an AI language model, I am a software tool that has been designed and developed with security in mind. The platform and infrastructure that host me are regularly monitored and maintained to ensure the highest levels of security.
However, it’s important to keep in mind that no system can be completely immune to security risks or vulnerabilities. While the development team responsible for me takes appropriate measures to minimize these risks, there is always a possibility that security breaches or attacks could occur.
Another critical consideration when deploying LLMs is credibility. Support teams rely on accurate information to provide high-quality support, and LLMs must be capable of providing it.
To ensure credibility, support leaders must choose a model that mitigates biases such as data bias (occurs when the data used to train the LLMs contains errors), confirmation bias (occurs when the model is trained on data that confirms pre-existing assumptions or biases) etc.
Support leaders can also deploy a hybrid approach that combines LLMs with human agents to help surface accurate information and build trust among customers. Additionally, they can use audit mechanisms, relevant engineering, and the like to increase customer trust.
Here is what ChatGPT has to say on this matter:
Are you credible?
“As an AI language model, I strive to provide reliable and accurate information to the best of my knowledge based on my training data. However, I’m not perfect and there is always a chance that my responses may contain errors or inaccuracies.
It’s important to keep in mind that my responses are generated by algorithms and are only as reliable as the data they are trained on. While I do my best to provide accurate and trustworthy information, it is always a good idea to verify the information I provide by cross-checking it with other sources or consulting with an expert in the relevant field.”
3. Domain Knowledge
Another important consideration when deploying LLMs in a customer support setting is domain knowledge. LLMs present results on the basis of the data they are trained on, thus they tend to hallucinate if the data is inaccurate or outdated.
For example, an LLM that is trained on a general dataset may not have the specialized knowledge required to effectively handle inquiries related to complex technical issues or specific industry regulations. This can result in inaccurate or incomplete responses that fail to address the customer’s needs.
Therefore, it is important to integrate your LLMs with a comprehensive solution that understands domain-specific knowledge and helps produce accurate results.
Here is a prompt to clarify:
How well-versed are you with domain knowledge?
“As an AI language model, I have been trained on a large corpus of text data and have been designed to understand and generate text across a wide range of topics and domains. However, my level of expertise in a specific domain depends on the quality and representativeness of the data I was trained on in that domain.
While I have access to vast amounts of information, I am not an expert in any particular domain. I can provide general information on a wide range of topics and can engage in conversations on many different subjects, but my responses should be verified by a human expert or cross-checked against other reliable sources if accuracy and reliability are crucial.”
4. Complex Actions & Defined Scope
LLMs are capable of performing complex actions such as natural language processing and sentiment analysis. However, support leaders must define the scope of the LLMs’ capabilities to ensure that they are used appropriately.
To define the scope, identify the tasks that their team wants to perform with LLMs and make sure they align with the goals. It is also important to identify areas that will benefit the most due to LLM integration and develop a strategy for the same and train your models accordingly.
Here is a prompt from ChatGPT to explain this:
Are you capable of performing complex actions?
“As an AI language model, I am capable of performing complex actions within my programming limitations. However, I cannot perform physical actions or manipulate physical objects like a human being. My abilities are mainly focused on generating text based on the inputs I receive and providing responses to queries. I can perform tasks such as generating content, answering questions, and completing text-based tasks, but my capabilities are limited to what has been programmed into me.”
5. Omnichannel Deployment
Finally, omnichannel deployment is another critical consideration when deploying LLMs in a customer support setting. Leaders must ensure that LLMs can be deployed across various channels, including email, chat, social media, and more. This requires ensuring that the LLMs are integrated with the organization’s existing communication channels and that they are capable of generating text that is appropriate for each channel.
This requires training the LLMs on data specific to each channel and providing them with access to relevant resources such as industry-specific style guides, etc. Additionally, customer support leaders should keep on updating their databases to produce faster and more relevant results and accordingly make the right choice.
Hear our dear buddy ChatGPT’s views on this:
Are your inputs consistent?
“As an AI language model, my inputs are generated based on the data and patterns that I was trained on. This means that my responses are consistent in the sense that they are generated based on the same underlying data and patterns, regardless of when or where the input was received.
However, it’s important to note that my inputs may not always be relevant or appropriate for the specific context or inquiry being presented. Like any software tool, I can only generate responses based on the information that is provided to me, and I may not always have access to all the relevant information or context needed to generate the most accurate or helpful response.”
Its Success and Failure Lies in Your Hands
Deploying LLMs into a business setting can be a significant step toward improving customer support and productivity. However, forward-thinking organizations often struggle with this. Thus, we bring to you a plug-and-play solution– SearchUnify that does it all with two LLM-infused products–SUVA and Knowbler.
Want to know how SearchUnify does it?
Go visit this session at SU Innovate and learn how to ride this new gen wave successfully!