Whitepapers
AI-based conversational assistant
Step by step, from the requirements request to the continuous improvement, how to develop a conversational assistant based on artificial intelligence.
1. Why are AI-based conversational assistants so important?
In a world where automated and high-level experiences are essential to business success, the importance of AI-based conversational assistants is undeniable.
From identifying requirements and objectives to continuous improvement iteration, here is a detailed step-by-step approach to building these key business tools.
2. Development guide
1. Identification of requirements and objectives
Before diving into the technique, it is essential to define the purpose, the type of questions to be answered and the scope of the virtual assistant. The same goes for the sources of information that we will provide so that it has the necessary knowledge and can give appropriate answers.
Also, it is necessary to define the channel in which the assistant will be available for users. It can be a frontend or it can be an app integrated to the chat (Slack, Whatsapp, Microsoft Teams, etc).
2. Data collection and preprocessing
There can be structured or unstructured databases or plain files such as PDFs. The important thing is to detect where the data sources are that will give context to the helper. From there, the processing to be done is defined.
3. Construction of the embedding
Embeddings are a collection of vectors that capture the essence of the content. This allows the files to be accessible and usable in real time by users. There are different embedding models, such as Microsoft Ada or Amazon Titan. We must select the one that best suits our requirements.
4. Creating a vector database
A specialized vector database such as Azure Cognitive Search or Amazon OpenSearch for storing and retrieving embeddings, allows users to quickly search for answers or suggestions based on semantic similarity.
5. LLM configuration and tuning
One of the most important steps. Design prompts that guide the model to generate answers aligned with the wizard’s purpose. To do this, experiment with different LLM models, analyze which one best fits the needs and configure the prompt so that the assistant behaves assertively. In some cases, fine-tuning is required to adjust the LLM model for the defined task.
6. Integration of the model with an API
For the wizard to be accessible and integrated on different platforms, the LLM model must be wrapped in an API, with tools such as FastAPI, Flask or Django. This should be able to receive an input from the user, process it through the model and return a response. It can then be deployed in a cloud environment for a secure and scalable application.
7. Interface development and application integration
If the use case requires it, a friendly and functional user interface (UI) can be developed for users to interact with the wizard. It can be a web app, a mobile app or an app that is integrated into other software. Then the necessary integration with backend APIs must be done to bring it to life.
8. Monitoring and alerts
Monitoring and logging tools track user interactions and wizard responses. Key performance metrics (KPIs) and thresholds for alerts are set. For example, if the wizard cannot answer a certain percentage of questions.
9. Iteration and continuous improvement
Data collected through monitoring allows users to identify areas for improvement. Feedback could also be required from users and from this feedback adjustments could be made.
This may include adjusting the prompt, fine-tuning the embedding or expanding the database with new sources of information.
These steps provide a solid framework for creating, implementing and maintaining an AI-driven conversational assistant.
3. Conclusions
– The first step before building an AI-based conversational assistant is to understand the value it will bring to the business.
– With that in clear, the requirements and the specific objectives and identification of the data sources that will give context to the assistant, will move forward.
– Then, embeddings are built to read the content of the documents and vector databases to search for answers or suggestions based on semantic similarity.
– The next step is essential: designing prompts that guide the model to generate answers aligned with the purpose. Once this is done, APIs are used to make the model available to users.
– The development of a user-friendly interface is the key to a greater deployment. However, thanks to monitoring and logging, it is possible to verify if the wizard is working as expected.
And this is just the beginning of the journey: there are always opportunities to improve the assistant.
AI-based conversational assistant: Development guide
Why are AI-based conversational assistants so important?
In a world where automated and high-level experiences are essential to business success, the importance of AI-based conversational assistants is undeniable.
From identifying requirements and objectives to continuous improvement iteration, here is a detailed step-by-step approach to building these key business tools.
Guide
1. Identification of requirements and objectives
Before diving into the technique, it is essential to define the purpose, the type of questions to be answered and the scope of the virtual assistant. The same goes for the sources of information that we will provide so that it has the necessary knowledge and can give appropriate answers.
Also, it is necessary to define the channel in which the assistant will be available for users. It can be a frontend or it can be an app integrated to the chat (Slack, Whatsapp, Microsoft Teams, etc).
2. Data collection and preprocessing
There can be structured or unstructured databases or plain files such as PDFs. The important thing is to detect where the data sources are that will give context to the helper. From there, the processing to be done is defined.
3. Construction of the embedding
Embeddings are a collection of vectors that capture the essence of the content. This allows the files to be accessible and usable in real time by users. There are different embedding models, such as Microsoft Ada or Amazon Titan. We must select the one that best suits our requirements.
4. Creating a vector database
A specialized vector database such as Azure Cognitive Search or Amazon OpenSearch for storing and retrieving embeddings, allows users to quickly search for answers or suggestions based on semantic similarity.
5. LLM configuration and tuning
One of the most important steps. Design prompts that guide the model to generate answers aligned with the wizard’s purpose. To do this, experiment with different LLM models, analyze which one best fits the needs and configure the prompt so that the assistant behaves assertively. In some cases, fine-tuning is required to adjust the LLM model for the defined task.
6. Integration of the model with an API
For the wizard to be accessible and integrated on different platforms, the LLM model must be wrapped in an API, with tools such as FastAPI, Flask or Django. This should be able to receive an input from the user, process it through the model and return a response. It can then be deployed in a cloud environment for a secure and scalable application.
7. Interface development and application integration
If the use case requires it, a friendly and functional user interface (UI) can be developed for users to interact with the wizard. It can be a web app, a mobile app or an app that is integrated into other software. Then the necessary integration with backend APIs must be done to bring it to life.
8. Monitoring and alerts
Monitoring and logging tools track user interactions and wizard responses. Key performance metrics (KPIs) and thresholds for alerts are set. For example, if the wizard cannot answer a certain percentage of questions.
9. Iteration and continuous improvement
Data collected through monitoring allows users to identify areas for improvement. Feedback could also be required from users and from this feedback adjustments could be made.
This may include adjusting the prompt, fine-tuning the embedding or expanding the database with new sources of information.
These steps provide a solid framework for creating, implementing and maintaining an AI-driven conversational assistant.
Conclusions
– The first step before building an AI-based conversational assistant is to understand the value it will bring to the business.
– With that in clear, the requirements and the specific objectives and identification of the data sources that will give context to the assistant, will move forward.
– Then, embeddings are built to read the content of the documents and vector databases to search for answers or suggestions based on semantic similarity.
– The next step is essential: designing prompts that guide the model to generate answers aligned with the purpose. Once this is done, APIs are used to make the model available to users.
– The development of a user-friendly interface is the key to a greater deployment. However, thanks to monitoring and logging, it is possible to verify if the wizard is working as expected.
And this is just the beginning of the journey: there are always opportunities to improve the assistant.
We would like to assist you in the development of your AI-based conversational assistants to enhance interactions with your customers
