Caso de Uso WGPT

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Use Case WGPT


Through the use of Artificial Intelligence, we have created a unique conversational interface that provides comprehensive information on a variety of products, offering instant answers and personalized recommendations to customer questions.

This conversational interface allows users to receive accurate and tailored information immediately. Our AI systems collect data from various sources and use the knowledge and experience of industry experts to create a comprehensive database of product information. This allows us to offer recommendations tailored to specific situations, giving users a satisfying experience when choosing the perfect product for each occasion.

We focus on understanding the individual needs and preferences of customers. Our advanced algorithms analyze the data and generate relevant, personalized responses. In addition, our conversational interface allows for a natural and fluid interaction, giving users an experience similar to speaking with a subject matter expert.


The user interacts with the AI interface, establishing a fluid conversation to ask questions and get instant, accurate and personalized answers that fit a consumer’s needs. Our system uses algorithms that take advantage of a vast database of information and expert knowledge to offer appropriate solutions to each query raised. The process is natural and intuitive, providing an easy, logical flow between questions and answers for the user.

By engaging with the AI interface, users can pose questions and promptly receive precise answers. Our system utilizes algorithms that harness a vast database of information and knowledge, enabling it to deliver suitable solutions for every inquiry.

Users initiate their queries through a dialogue with the AI interface, aiming to obtain answers aligned with their specific requirements. Leveraging accessible data, the system will generate the most effective responses attainable.


  • Define the data with which the model will be trained.
  • Data processing prior to using it with GPT:
    • Data cleaning: This is the first step and may involve removing duplicate data, correcting errors, dealing with missing data (eliminating it or imputing it with a value such as the mean or median), and removing outliers.
    • Data transformation: It may be necessary to transform the data to make it more useful for the model. This can include tasks such as normalization or standardization, which converts the data to a common scale, or one-hot coding, which converts categorical variables into a form understandable to the model.
    • Dimensionality reduction:In some cases, there may be data with a large number of features, some of which may not be useful to the model. Techniques such as Principal Component Analysis (PCA) help reduce the dimensionality of data.
  • Exploratory data analysis:
    • Univariate analysis: this analysis examines each variable individually. For continuous variables, you can analyze the mean, median, range, etc. In the case of categorical variables, the frequency of the different categories can be analyzed.
    • Bivariate analysis: This analysis examines the relationships between pairs of variables. Correlations, scatterplots, boxplots, etc. can be used.
    • Multivariate analysis: This analysis examines the relationships among more than two variables at the same time.
    • Data visualization: Creating graphs and figures can help you understand your data and discover patterns, trends, and relationships. It can include histograms, scatterplots, box-and-whisker plots, heat plots, etc.
  • Perform the training / embedding of the data.


  • F1 Score:For tasks like named entity recognition or question response, you can use the F1 score which is a measure of accuracy and completeness combined into a single metric. It is useful for evaluating the quality of a classification model or a detection algorithm. A higher F1 score indicates better model performance.
  • BLEU (Bilingual Evaluation Understudy): This metric is used in machine translation tasks and compares the model output with one or more reference translations.
  • ROUGE (Recall-Oriented Understudy for Gisting Evaluation): Used to evaluate summary generation models.
  • Human Evaluation: Often, human evaluation plays a crucial role in determining the usefulness and quality of language models. Humans can evaluate aspects such as the coherence, relevance and naturalness of the generated text.


Limited Context: GPT models, even newer versions like GPT-3 and GPT-4, have a limited context window, which means that they can only consider a certain number of tokens (e.g., words or characters) at a time. This can lead to responses that do not consider all of the previous conversation.

Generation of False Information: GPT models do not have the ability to verify the veracity of the information they generate. This means that they can generate false or incorrect information.

Lack of Deep Comprehension Although GPT models can generate responses that appear to understand and reason about the world in a similar way to humans, they do not actually understand the meaning of the words and phrases they generate. Their "understanding" is based on patterns in the data they were trained on.

Bias in Training Data: GPT models can reflect and perpetuate biases present in the data on which they were trained. This can lead to answers that are biased or inappropriate.

Want to know more about this and other use cases?

Talk with one of our experts and join us on this journey.