Assistants created with OpenAI: Co-pilots for business growth

User experience is being taken to a new level. Intelligent virtual assistants created with OpenAI incorporate generative artificial intelligence (AI) and change the rules of the game. They enable natural language interactions and follow a fluid conversation while keeping things in context.

And, most importantly, they are highly effective at solving tasks. By the very nature of these models, they are able to interpret natural language and answer specific questions about information stored in documents, audio and images. In addition, they have been trained on a huge volume of data of different kinds, so they cover a wide range of topics. Despite their size (and also thanks to that), they have a very good performance in providing accurate answers, optimizing the search and extraction of relevant information.

But there are also mechanisms to complement the training of these assistants with the company’s own data. Thus, it is possible to extend the knowledge that the LLMs (Large Language Models) have. In this way, the power of LLMs is leveraged and combined with the company’s own data sources. This makes it possible to solve tasks and meet specific business needs.

Wizards created with Open AI: Use cases

The use cases around intelligent virtual assistants are numerous and go far beyond “customer care”. They can generate original and personalized content or process very extensive and technical information to make it understandable for operators or collaborators with less knowledge or experience.

In education, they allow students to ask questions or obtain summaries of recorded lectures. Teachers, meanwhile, can upload student reports or review an academic record.

Healthcare professionals speed up the reading of clinical studies and the analysis of a patient’s medical history. And security experts have the option of detecting potentially dangerous elements. This, from videos or images on public roads or inside companies or institutions.

In banking and fintech, they are used for processing payment receipts or invoices through images, with extraction of relevant information. They are also used to analyze images or audio recordings to detect possible fraud, such as document forgery or identity theft attempts.

And in contact centers they can be used to extract valuable information about customer service or possible problems or dissatisfaction. These are just a few examples. It is a wide field that deserves to be explored.

Step by step to create an assistant with OpenAI

As in any initiative, the first thing to define is the challenge to be solved, whether it is a business need, a pain point or an opportunity to improve operational efficiency. Then, it will be necessary to ask if there are manual processes and tools within the company that already address that problem.

Also look for data or examples within the organization that can be used to extend the LLM’s knowledge. Remember that technology and models are available to all companies, including competitors. So it is important to focus on how to use our data in conjunction with LLMs to get results that will enable us to solve a task or satisfy our customers.

As with any AI and data-driven solution development, it is recommended that an evaluation of modeling options be done to identify which one best fits the need. There is a wide range of models of different sizes and, therefore, with different performances and costs. Smaller and cheaper models can be used to perform simple tasks, while for more complex tasks (such as orchestrating a conversation) a larger and more expensive model can be used.

If the results obtained with the chosen model are good, it is time to scale the solution, focusing on priority functionalities or those that form an MVP (minimum viable product).

The control mechanisms and how the user gives feedback on the tasks that the wizard solves should be considered. This is a key practice within MLOps (acronym for machine learning and operations) but focused on LLM models (also known as LLMOps) to monitor, safeguard and adjust the behavior of the model in a productive stage. It is key in this type of solution to ensure that the model responses are safe and perform their intended task.

OpenAI wizard development challenges

First of all: these models are not determined. Executing more than one action with the model with the same input does not always give exactly the same result.

This leads to a paradigm shift. The approach to the development of solutions using these technologies must be based on how to deal with model failures or responses that do not match.

For this, solutions must have a human-in-the-loop approach, i.e., how do we make it possible for humans to control, validate, adapt and review those responses generated by the model that can be considered unsafe or that do not meet the requirements.

Another important aspect is the lack of maturity in terms of data platform or the very lack of data when we want to extend the knowledge of the LLM. In this case, there is a recommendation when adopting, scaling and operating this type of solutions. Focus on developing capabilities, tools, policies and processes that increase maturity in data governance and exploitation. Also, some engineering or data collection work may be required.

Finally, when there are uncertainties as to whether the technology can actually solve the problem, an incremental approach is recommended to reduce technical risk. This is often achieved by mixing pilots and exploratory data analysis.

Benefits of OpenAI wizards

The ability to solve time-consuming tasks in an automated or assisted manner generates efficiencies and increases the capabilities of both individuals and teams. Time that used to be spent on these tasks is now used for higher value-added activities.

For end customers, replacing unintelligent or low-performing bots with LLM-based assistants, with greater understanding, context, memory and consistent responses, can increase satisfaction levels, achieve a higher NPS and in turn reduce support costs.

Finally, LLM-based assistants are scalable, so they offer the possibility of accompanying the company’s growth.

Conclusions

The impact of generative AI on organizations is very large. Capitalizing on this opportunity is, for companies, a passport to the future.

The support of a technology partner such as Nubiral can be key to ensure that virtual assistants deliver the maximum added value and generate the greatest possible impact on the business. And thus become the co-pilots of our growth.  

Are you interested in incorporating intelligent assistants to take your business to a new level? Schedule a meeting!