As you know, in Capitole we have presence in many different industries. Many of us are in data processing projects, in Data Science / Development /Devops positions and work both on physical servers and on cloud machines in AWS, Azure or other cloud services. For us it is very important to work efficiently and follow good practices in development, leaving a good image of our company wherever we go. This allows us to perform our job the best we can and makes things easier for the end customers of the developed product.

In this article, we share some of the thoughts that we have acquired over time, that are meant to help as tips to organize the code. They are simple tricks that can save a lot of time and misunderstandings in the day-to-day work of the team of developers.

Inline Tests

I know you test your code; otherwise, how do you know it works? But here’s the question: do you keep track of the tests you do? If not, how can others trust your code?

Welcome to the amazing world of unit testing. This is one of those things that might not seem fun at the beginning, but once you’ve experienced long hours wasted debugging code, and then hours saved thanks to testing your code, it magically becomes fun and a must.

I would want to teach you about the assert statement, also known as “inline tests”. These tests are useful to check if the input and output of your functions are correct.

Let me show you an example where this comes in handy. Let’s say you are working with a vector of probabilities, and you want to project to 0 or 1 depending on a threshold. This function is implementing this:

def project_to_zero_or_one(probabilities, threshold):

# define empty array

projections = np.empty_like(probabilities)

# project

projections[probabilities < threshold] = 0

projections[probabilities >= threshold] = 1

return projections

But what if there are nans in your input vector? What if one of the entries is <0 or >1? (remember probabilities are not defined outside the range [0,1]) What if the input is a matrix and not a vector?

I would like the code to tell me if anything like that is happening, meaning there’s something wrong somewhere else I need to fix before it’s too late.

def project_to_zero_or_one(probabilities, threshold):

# check input

assert probabilities.ndim == 1, « Input must be a vector! »

assert np.isnan(probabilities).sum() == 0, « Input contains NaN values! »

assert np.sum(probabilities > 1) == 0, f »There are probabilities > 1! »

assert np.sum(probabilities < 0) == 0, f »There are probabilities < 0! »

# define empty array

projections = np.empty_like(probabilities)

# project

projections[probabilities < threshold] = 0

projections[probabilities >= threshold] = 1

return projections

One practice I like to follow is extracting all assert statements out of the main function. This is particularly useful when you have other functions that use the same argument, such as probabilities, allowing you to reuse the code.

def _check_probabilities(probabilities):

assert probabilities.ndim == 1, ‘Input must be a vector!’

assert np.isnan(probabilities).sum() == 0, ‘Input contains NaN values!’

assert np.sum(probabilities > 1) == 0, ‘There are probabilities > 1!’

assert np.sum(probabilities < 0) == 0, ‘There are probabilities < 0!’

Code formatters and Stylers

You may not realize it yet, but you’ll spend most of your career reading code instead of writing it. Whether you work in a team and review your colleagues’ code, or when you are trying to solve a problem by looking for an answer on StackOverflow, or even when you come back to debug code you wrote months ago. In all those situations, you will be reading a lot of code.

For that reason, it is important to write code in a consistent and uniform way. This includes decisions such as maximum line length, empty lines between function definitions, and syntax conventions like vector[:-1] or vector[: -1]. These may seem like small details, but they have a significant impact on code readability for humans. The big question is, can all these small decisions be automated? Yes, indeed.

• A code formatter is a tool that automatically modifies the layout and style of source code to adhere to a specific set of formatting rules or guidelines. I highly recommend Black.

• On the other hand, a code styler is a tool that assists developers in applying a specific coding style or set of guidelines to their code. While similar to code formatters, code stylers are more flexible and suggest changes to the code instead of modifying it directly. For example, they may suggest renaming variables or removing unused libraries. I highly recommend flake8.

Structuring code as a package

Are you having trouble importing your own Python modules? Does the error ModuleNotFoundError: No module named ‘my_python_file’ look familiar? Have you already experienced the insecurity of knowing if you have installed your modules, where they are located or if you are using the correct path? It might be time to improve your code structure.

Whenever starting a new project, structure your code something like this:

my_project/

├── src/

│ ├── __init__.py

│ ├── my_module.py

│ └── my_folder/

│ ├── __init__.py

│ └── my_other_module.py

├── data/

│ ├── raw/

├── scripts/

│ ├── my_script.py

├── setup.py

└── README.md

A few things to note:

•  When Python imports a package, it looks for the __init__.py file in the package directory and executes any code inside it.
• setup.py is a Python script that is used to define the metadata and dependencies of a Python package. The simplest it can be is:

from setuptools import setup, find_packages

setup(

name=’my_package’,

packages=find_packages(),

)

You can also specify dependencies, authors, versions, etc:

from setuptools import setup, find_packages

setup(

name=’my_package’,

version=’0.1′,

author=’John Doe’,

author_email=’john.doe@example.com’,

description=’A simple Python package’,

packages=find_packages(),

install_requires=[

‘numpy>=1.16.0’,

‘pandas>=0.23.4’,

],

)

Once your folders look like this (and you are in your virtual environment) type pip install -e path/to/my_project/. This will install your package in editable mode. This means that as you change your code your installed package is automatically updated, and you won’t need to reinstall anything.             

Conclusion

In summary, good coding structure and practices not only improve development efficiency, but also facilitate collaboration and long-term code maintenance.

• The practice of testing (in an ordered and consistent manner) is essential to ensure in a reliable and controlled way that the code complies with the defined functionalities correctly.
• The use of code stylizers and formatters are essential habits to homogenize criteria in any development team. The key is to write code that is easily understandable, replicable, and adaptable, which will benefit both you and your teammates and customers.
• Structuring your own code as a package is a good practice that will make it easier to share and publish the code in the future and installing it in editable mode saves a lot of time, as it updates automatically.

Efficiency in code is ultimately efficiency in results.

The post Code Development Tips: Unit tests, code formatters and stylers and structuring code as a package. appeared first on Capitole.

Understanding the Synergy

Agile methodologies, with their iterative approach and focus on continuous improvement and customer feedback, align perfectly with the rapid evolution of AI. Here, it’s essential to clarify that we are primarily referring to Generative AI and Predictive AI. Generative AI, such as natural language processing and content generation models, enables the creation of new content, while Predictive AI uses Classical Machine Learning (ML) algorithms to analyse historical data and make predictions. These approaches allow AI to process vast amounts of data, augment human capabilities, automate repetitive tasks, and provide valuable insights to inform decision-making.

Key Areas Where Classical Machine Learning Can Enhance Agile Practices

Predictive Analytics for better planning: For accurate forecasting machine Learning algorithms can analyse historical data to predict future trends, aiding teams allocate resources correctly and estimate effort more accurately.

Risk mitigation: Because ML can identify potential bottlenecks early on teams can proactively adjust their plans and allocate resources effectively

 Self-Healing Tests: Machine Learning-powered testing frameworks can automatically adapt to code changes ensuring continuous quality and reducing time spent on regression testing.

Accelerated Development: ML models can generate entire functions based on natural language descriptions or code patterns which in turns speeds up development cycles.

Improved code quality: ML-driven refactoring tools can identify code smells, suggests improvements, and automatically apply refactorings, enhancing code readability and maintainability.

Intelligent code completion: ML-powered code completion tools can suggest necessary code snippets and functions based on context reducing typing effort and improving developer productivity.

If you are considering integrating Machine Learning to development teams, it is however important to take into consideration the following.

• Ensure that data is accurate, clean and complies with privacy regulations.
• Make ML models transparent and explainable to foster trust and accountability.
• Regularly update and retrain ML models to keep pace with evolving requirements and data.
• Finally foster an environment of collaboration between ML experts and software developers to ensure seamless integration.

While both Machine Learning (ML) and Artificial Intelligence (AI) are closely related and often used interchangeably, they have distinct characteristics and applications within Agile software development.  

Machine Learning is a subset of AI that focuses on algorithms that allow computers to learn from data without explicit programming. It involves training models on large datasets to recognize patterns, make predictions, and make decisions.  

AI, on the other hand, is a broader field that encompasses various techniques and technologies, including machine learning, to simulate human intelligence.  

Key Areas Where AI Can Enhance Agile Practices

Here are specific examples of how AI can be applied in Agile environments, along with the type of AI most relevant for each use case:

• Generating User Stories: AI can help generate initial drafts of user stories from business requirements, accelerating the creation of product backlogs.
• Automating Test Cases: AI models can automatically generate test cases based on code changes and requirements, significantly reducing the time spent on manual testing.
• Predicting Project Timelines: Predictive AI can analyse historical data from previous projects to predict delivery timelines and identify potential risks ahead of time.
• Improving Code Quality: AI-powered tools can detect defects in the code, suggest improvements, and automate code reviews, enhancing the overall quality of the software.
• Automated Documentation: Generative AI can help automatically generate accurate, up-to-date documentation, reducing manual effort and ensuring consistency. Models like GPT (Generative Pre-trained Transformers) can assist in creating technical documentation or progress reports from raw data, ensuring high coherence and accuracy.
• Improved Collaboration: AI-powered collaboration tools such as virtual assistants and recommendation systems can enhance communication and knowledge sharing among team members, even in remote settings. These tools help streamline problem-solving and knowledge transfer across distributed teams, Teams Copilot is an excellent and specific example we can use here, it is capable summarising meetings using recorded transcripts from concluded meetings.
• Enhanced Decision-Making: AI-driven insights can help Agile teams make better data-driven decisions regarding product backlogs, resource allocation, and risk mitigation. Combining Predictive AI with data analytics, teams can make more informed decisions based on real-time insights and historical data.

Let’s look at specific applications of AI in Agile that can drive efficiency and improve results:

Prompt Engineering: Optimizing AI Interaction

Prompt Engineering refers to the art of crafting clear and effective prompts to guide Generative AI models in producing the desired output. Below are key recommendations for getting the best results when working with AI in Agile projects:

• Be Specific: Clearly articulate the desired outcome of the AI-generated content.
• Provide Context: Background information is crucial for the AI model to understand the task.
• Define the AI’s Role: Indicate the specific role the AI should take when generating results (e.g., « Act as an expert scrum master with the objective of finding a permanent solution to the consistent problem of technical debt of a development team that is mature in agile methodologies give me a list of immediate actions to take, let your writing style be narrative and your tone persuasive”).
• Identify the Target Audience: Tailor the AI’s response to the needs of the end user, whether it’s a development team or a customer.
• Set a Clear Objective: Ensure the model understands the goal it needs to achieve.
• Establish the Tone and Style: Decide on the tone (formal, persuasive, cooperative) and writing style (narrative, descriptive, etc.).
• Experiment and Adjust: Continuously refine the prompts based on the results to improve the quality of the responses.

Conclusion: The Future of Agile with Generative AI

The combination of Agile and AI is transforming the way we work, unlocking new levels of innovation and continuous improvement. By adopting AI, Agile teams can deliver faster, more accurate results that are aligned with customer expectations.

At Capitole, we are at the forefront of digital transformation, helping our clients harness the power of Generative AI to optimize their Agile processes. If you want to maximize the value of your Agile teams with AI-driven solutions, reach out to us today. We’re here to guide you on this exciting journey toward the future of work.

Sources

• TensorFlow: https://www.tensorflow.org/
• Papers with Code: https://paperswithcode.com/
• Machine Learning is Fun: https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471  
• https://github.com/mananahmed/sepoy-twitter-archive
• Agile Alliance: https://www.agilealliance.org/
• Scaled Agile Framework (SAFe): https://scaledagileframework.com/
• arXiv: https://arxiv.org/ , Scikit-learn: https://scikit-learn.org/
• Google AI Blog: https://ai.google/latest-news/
• PyTorch: https://pytorch.org/

The post AI-Powered Agile: The Future of Work appeared first on Capitole.

Strategic Adoption of GenAI

One of the common challenges faced by product managers and product owners is being unable to fully engage in their roles and instead becoming mere intermediaries between business requirements and the development team. This often happens because they lack the time, authority, or tools to perform their duties comprehensively. Moreover, technical debt and bugs frequently siphon team capacity when planning hasn’t accounted for these appropriately.

For product managers and product owners, GenAI is a game-changing tool to:

• Identify complex patterns: Analyze vast amounts of data and market trends.
• Generate structured information: Compile detailed materials from various sources in less time.
• Focus on active listening: Free up time for high-value activities like iteration and user feedback.

By leveraging GenAI, you can take charge and provide stakeholders with actionable insights, enabling the creation of new features and functionalities that deliver true value to users. Moreover, these tools help uncover new use cases or automations that improve product quality and prevent disruptions impacting users.

Efficient adoption of GenAI starts with mastering prompt engineering. The quality of the outcomes depends on how clearly we communicate with the tools. Models like Sara Tamsin’s (Context – Task – Instruction – Clarification – Refinement) or Kyle Barner’s RISEN framework (Role – Instructions – Steps – End goal/Expectation – Narrowing/Novelty) provide practical guidance for crafting effective prompts. For more on prompt engineering, consult OpenAI’s comprehensive documentation

Foundational Use Cases of GenAI in Roadmap Optimization

• Predictive Analysis: Anticipate the impact of future features using algorithms based on historical data. Ask GenAI tools to draw insights from specialized sources, reports, and studies or to analyze user surveys and detect patterns.
• Backlog Automation: Use tools like ChatGPT to efficiently draft epics and user stories.
• Story Mapping: Organize user stories visually to streamline sprint planning.

Advanced Use Case: Building a Comprehensive Roadmap with AI

For a deeper level of application, consider using a GenAI tool, like the widely adopted ChatGPT, as a genuine copilot by feeding it all relevant context and knowledge about your current role. Two potential scenarios could guide this approach:

1. Starting a new business model: You’re a PO entrepreneur creating an MVP.
2. Evolving an existing product: You’re enhancing and implementing new functionalities or processes.

In both cases, the approach involves setting up a custom ChatGPT or maintaining a document that consolidates all the relevant information. Continuously attach and reference this document in your prompts to ensure it serves as a reliable source.

Step 1: Define the Product Vision

Ask the AI to generate a product vision by providing context and objectives. Refine the results until you achieve a solid vision statement, core functionalities, and unique value propositions.

Step 2: Identify Target Personas

The AI can create detailed profiles of potential users. Provide the AI with background information, and within seconds, it can deliver 4–5 personas, complete with needs, interests, and preferences.

Step 3: Generate Jobs to Be Done (JTBD)

Using the defined personas, ask the AI to identify JTBD aligned with your product’s functionalities.

Step 4: Create Epics and User Stories

From the JTBD, prompt the AI to generate epics with acceptance criteria and break them into detailed user stories. Keep saving this information to the reference document for consistency in subsequent prompts.

Step 5: Story Mapping and a Complete Roadmap

With all the user stories, instruct GenAI to create a partial delivery map. In minutes, you’ll have a structured roadmap ready to tailor to your product’s specific needs.

Incorporating this technique into your routine boosts productivity and hones your skills as a meticulous product owner. However, it’s crucial to remain aware of the rapid pace of technological advancements and continuously update your knowledge.

Maximizing GenAI’s Value in Product Management

1. Ongoing Training: Stay updated on the latest features and best practices.
2. Regular Assessment: Periodically evaluate GenAI’s impact to uncover areas for improvement.
3. Balanced Approach: Use GenAI to complement, not replace, human judgment.

Capitole prioritizes continuous learning, enabling each team member to remain at the cutting edge of technology. Leveraging such opportunities is essential for enhancing productivity and advancing toward truly strategic product management. Capitole can also help you maximize your roadmap definition, with or without GenAI, as experts in this area.

We’re witnessing a quiet revolution that’s reshaping the product owner’s role. Integrating GenAI isn’t optional—it’s imperative for those aiming to lead innovation. The future of product development is being written today, and GenAI is the pencil sketching the brightest lines.

The post Optimizing the Product Roadmap with Generative AI Tools appeared first on Capitole.

Digital transformation is the process through which an organization adopts new technologies across all its operations.

This is precisely one of the areas in which Capitole offers advanced knowledge and expertise in digital environments, with the goal of driving substantial improvements and progress at every area and level within companies.

What is change management, and how is it integrated into digital transformation?

Change Management is a fundamental discipline in any organization that aims for continuous improvement. It can be applied to specific events within companies, such as changes in organizational structure and culture or the implementation of new technologies and work methodologies.

In this article, we focus specifically on the relevance of change management within software development teams. The main objectives of change management are:

• Minimize resistance to change among end-users.
• Reduce negative impacts on work teams.
• Promote the commitment of all involved parties.
• Provide greater recognition for the work of the teams.

With this, we highlight how change management focuses mainly on the human aspect of digital transformation.

Key Components of Change Management

There are different change management models, such as Lewin’s model, the ADKAR model, and Bridges’ transition model. All of them consider key aspects to ensure the success of the initiative.

Effective Communication

A change management strategy must clearly explain the reasons, benefits, and expectations of the change. This is aimed at conveying transparency and building a foundation of trust.

Here, it is important to leverage tools like Slack or Teams, which promote more collaborative work and benefit the change process.

1. Effective Communication

A change management strategy must clearly explain the reasons, benefits, and expectations of the change. This is aimed at conveying transparency and building a foundation of trust.

Here, it is important to leverage tools like Slack or Teams, which promote more collaborative work and benefit the change process.

2. Training and Support

It is also key to ensure that employees clearly understand how the change impacts each of their roles and have effective tools available to help them adapt.

At this point, organizing training for the teams involved in the change is crucial to make the adoption of new technologies more effective.

3. Participation and Feedback

A change management model should encourage teams to express their concerns and suggestions. This can be done through forums and regular events that promote the benefits of the newly adopted technologies or work models.

4. Evaluation and Adjustments

It is very important to measure the impact of the change, evaluate the results, and continuously adjust the strategy as new needs arise.

How can change management support development teams?

Change management within a software development team is not only focused on adopting new technologies but also on integrating new forms of collaboration, as well as transforming mindset and organizational culture. All of this aims to keep the team aligned with industry best practices.

With the rapid evolution of software development, it is essential that tools, frameworks, and methodologies are kept up to date.

A change management strategy not only promotes better practices within development teams but also positively impacts the end user.

However, without an appropriate change management strategy, resistance among those involved may arise. Why?

1. Lack of Knowledge or Training

When employees or users do not understand why new tools or processes are being implemented, or if they have not received the proper training, they may feel that this change will complicate their work.

2. Short-Term Loss of Efficiency

Adopting new technology involves a learning curve. Initially, people may feel less productive and doubt whether the change will bring benefits.

3. Lack of Visibility and Recognition

Team members may feel that their work is not valued enough if the impact of the change is not communicated effectively. This ultimately also affects the perception of end users, who may be unaware of the benefits of new features or improvements in their products.

As a UX/UI designer, my role in change management is crucial in creating a visual, attractive, and functional transition that facilitates the adoption of new tools or processes by end users. Additionally, it helps developers feel supported and valued during the process.

Practical Cases of UX/UI Content in Change Management

Imagine that the company has developed a new software platform that will replace the previous one. This change will affect both developers and end users. An effective Change Management process could include:

• Introductory Video: A video introducing the new platform, visually explaining its benefits and improvements over the previous one.
• Guided Tutorials: Create a set of short tutorials explaining how to use the new features and guiding the user through their first experience with the platform.
• Feedback Spaces: Implement an option in the interface where users can leave feedback on the platform, which helps improve the perception of the change and make real-time adjustments.

Benefits of Good Change Management

1. Increased Productivity: With a well-managed transition, developers can quickly familiarize themselves with the new tools or methodologies, reducing the impact on their productivity.
2. Reduced Resistance to Change: Good Change Management minimizes resistance by allowing developers and users to understand and appreciate the improvements.
3. Visibility and Recognition: Through UX/UI content, the work of developers becomes visible, which is motivating and contributes to a positive work environment.
4. Sustainable Adoption: When users are properly trained and informed, the adoption of new tools or features is more lasting and effective.

Conclusion

Change Management is an essential process in software development teams, especially when it involves adopting new tools, technologies, and work methodologies.

From the perspective of a UX/UI content designer, the role in Change Management is strategic, as it facilitates visual communication that helps developers adapt and allows end users to effectively adopt new features.

At Capitole, we assist organizations in effective and adaptable transitions for different teams, promoting best practices aimed at a future-oriented digital transformation.

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But what exactly is a Large Language Model? How does this technology work and what are its limitations? What are the uses of this technology in the business world? In the following article we will provide answers to these questions:

What exactly is a Large Language Model ?

An LLM is a natural language model formed by deep neural networks. Its neural networks have been trained on large amounts of data.

The application of statistical and prediction models to natural language is not new.

In the 1980s and 1990s with n-grams and hidden Markov models, the application of probabilistic mathematics to language was developed, giving rise to a variety of tools and methods for creating more flexible data-driven mathematical models.

But it was not until recently that this technology was truly consolidated with the discovery of the Transformer by Google experts, presented in the famous paper “Attention is all you need”. The Transformer is a neural network that attempts to mimic the attention we humans pay to the context of a word or set of words in a body of text. Let’s see it with an example:

When we read the previous paragraph we establish a relationship between the words coco – perro – patas – jugar. If we only read the last sentence (Coco likes to play tag), we do not know if Coco is a dog or a person. However, thanks to our inherited human attention we take into account the context of the whole paragraph. This is how the Transformer created by goodle calculates the relevance between different words in a text corpus.

This discovery led to ChatGPT3, a chatbot based on the foundational Generation Pretrained Model 3 (GPT-3) that revolutionized the world, becoming the chatbot with the highest active user growth in history. Composed of a neural network with 175 billion parameters, it is capable of generating text, understanding language and answering questions in a surprising way.

These capabilities such as reading comprehension, logical inference or even more advanced tasks for a machine, for example explaining why a joke is funny, would be within the reach of the densest models.

Does this mean the end for humans, and will AI take away our jobs as everything can be automated by these models? Not yet, says Meta’s Chief AI Scientist, Yann Lecun in this interview; LLMs have several limitations that make them unreliable if they are not accompanied by the necessary software architectures.

What are their limitations?

One of the major limitations LLMs have is that they are not able to generate data that is outside the training set. For example, if you ask ChatGPT who Steve Jobs is, it will provide an answer about the famous tech entrepreneur. However, if you ask it about the latest sales made in your company’s sales department, it will not be able to give you an accurate answer. This happens because LLMs do not have direct access to the most up-to-date information happening in the world.

But if we give these Chatbots, connected to LLMs, access to the right context, they would be able to answer any kind of question accurately thanks to their writing power and linguistic understanding.

This is why a new software architecture has recently emerged that manages to solve the aforementioned problem. It is called Retrieval Augmented Generation (RAG) and connects a database with a search engine that contains everything relevant to the user. In this way the LLM will be able to access information that he/she was not trained on.

This turns the problem of the lack of context of LLMs into a problem of information management and search, whose solutions have long been studied and developed in the information sector.

The infrastructure describing a RAG architecture is typically composed of:

• An Ingestion Pipeline that injects and fragments the documents into different parts, commonly called chunks. This pipeline will help us to implement different document fragmentation strategies depending on the data they contain.
• The pipeline will connect with an embedding model to vectorize back and forth the input and output data from the database. These models convert document fragments into sophisticated numerical representations.
• Finally, a vector database, which stores and indexes the information for later retrieval. The most common metric for searching and successfully answering user queries is cosine similarity.

Therefore, by basing answers on up-to-date data, RAG reduces the chances of generating incorrect information in the form of hallucinations, because of the tendency to always answer queries. In addition, fine-tuning or re-training of the model for specific knowledge areas (such as apps with knowledge of mining practices or logistics of fashion products) could be investigated. Updating the database may be sufficient in general use cases but there is scientific literature indicating that LLM fine-tuning can increase the accuracy of the RAG-enhanced application.

However, it is also important to identify some disadvantages:

• The effectiveness of the RAG architecture depends heavily on the quality of the search engine configuration, as well as on a good document preprocessing strategy: choosing the right embedding model.
• The contextual message of LLMs is limited: the amount of text with instructions and practical examples for the AI to perform its function. According to the scientific literature when the size of the context increases, the attention span of the actions performed by the models decreases. Therefore, we will have to write the messages following prompt engineering’s expert recommendations to make sure that everything is interpreted and nothing escapes the LLM’s attention.
• There is a notable evaluation difficulty: evaluating a RAG application is difficult due to the non-deterministic or random nature of LLMs which makes the quality of the information generated variable if the application is not properly tuned. Given the difficulty in applying traditional metrics, continuous evaluation and monitoring of these applications is required.

In conclusion, the combination of Large Language Models (LLMs) with the Retrieval-Augmented Generation (RAG) architecture has marked a breakthrough in the area of Natural Language Processing by mitigating some of the key limitations of LLMs, such as hallucinations and access to updated information. RAG improves the accuracy of LLMs by integrating a search engine, without incurring LLM retraining costs. However, the success of this solution depends on the robustness of the vector database search engine and the availability of relevant information.

LLMs can automate repetitive tasks, improve customer service and facilitate content creation, allowing your team to focus on strategic decisions. However, not all tasks benefit from LLMs. For deep analytics or very specific data-driven decisions, RAG can complement the model by providing up-to-date context.

If you want to learn more about how these technologies can transform your business, contact us at Capitole. Our team will help you identify the most effective applications to optimize your daily operations and make the most of artificial intelligence, as well as develop predictive models.

The post What are LLMs and what are their limitations? appeared first on Capitole.

Unleash Your Business Potential with Low-Code Innovation!

In today’s ever-evolving business world, speed and adaptability are not just advantages – they’re necessities. Imagine a game-changing solution that allows your organization to swiftly adapt and automate processes effortlessly. Enter the world of low-code platforms – a revolutionary approach to Business Process Automation (BPA) that ensures efficiency without the need for extensive coding expertise. Let’s explore how this can transform your business!

The Low-Code Revolution

Low-code platforms are swiftly becoming the go-to solution for businesses aiming to streamline and automate processes. The beauty lies in their ability to empower both IT and non-technical users to collaboratively build applications using a visual development interface, slashing development time significantly.

Accelerated Development Cycles

Traditional software development can be time-consuming, causing delays in implementing crucial business processes. Low-code platforms revolutionize this by offering a visual, drag-and-drop interface. This means your team can create applications with minimal coding, resulting in faster deployment and the ability to adapt swiftly to changing market demands.

Increased Efficiency and Productivity

Unlock enhanced efficiency and productivity by automating repetitive and time-consuming tasks. Low-code platforms free up valuable human resources, allowing your team to focus on strategic, creative, and high-value activities. Say goodbye to errors and hello to dedicated time for critical thinking and innovation.

Democratizing Innovation

Low-code platforms break down barriers to innovation. No longer confined to IT experts, users across departments can contribute to the development process, fostering a culture of innovation throughout your organization. Silos crumble as collaboration takes center stage.

Cost-Efficiency and Scalability

Implementing low-code platforms leads to substantial cost savings. With reduced need for specialized coding expertise and faster development cycles, project costs plummet. Additionally, these platforms offer scalability, allowing your business to adapt and grow without hefty resource investments.

Ensuring Future-Proof Operations

In the dynamic business landscape, adaptability is crucial for long-term success. Low-code platforms provide a future-proof solution, enabling businesses to iterate and update applications rapidly. This agility ensures your organization can keep pace with evolving market trends and technology advancements without massive overhauls.

Realizing the Potential: Use Cases and Examples

Let’s delve into real-world examples of how companies can leverage low-code technology to address specific challenges, promote collaboration, and unlock new opportunities for innovation and growth:

1. Customized CRM Systems: Swiftly enhance customer interactions with a tailored CRM system, collaboratively built by marketing and sales teams using low-code platforms.
2. Supply Chain Optimization: Streamline supply chain processes by automating workflows, allowing logistics and procurement teams to collaborate seamlessly.
3. Employee Onboarding and HR Workflows: Boost HR efficiency by automating onboarding processes. Low-code technology ensures a seamless onboarding experience for new hires.
4. Data Integration and Reporting: Overcome disparate data challenges with integrated dashboards and reporting systems created by cross-functional teams, no extensive coding required.
5. Regulatory Compliance Solutions: Stay compliant effortlessly by adapting systems to new regulations swiftly, courtesy of low-code platforms.
6. E-commerce Platform Enhancement: Stay competitive by enhancing e-commerce platforms collaboratively with marketing, sales, and IT teams using low-code technology.
7. Healthcare Process Automation: Improve operational efficiency in healthcare with rapid development of applications for appointment scheduling, patient records, and billing.
8. Educational Institution Management Systems: Streamline administrative processes in educational institutions with applications for student enrollment, grading systems, and communication channels built using low-code platforms.

Embrace the Low-Code Revolution with Capitole Consulting

At Capitole Consulting, we are at the forefront of this technological revolution. Our team of knowledgeable experts is dedicated to helping companies build the fundamentals, preparing your organization to adapt and transform seamlessly. Are you ready to unleash the full potential of your business through low-code platforms? Join the revolution with Capitole Consulting and propel your organization into a new era of efficiency, innovation, and growth. Let’s shape the future together!

Referencias:

• ¿Sabes lo que es ser un Citizen Developer? – Software Testing Bureau

• Microsoft is a Leader in the 2021 Gartner® Magic Quadrant™ for Enterprise Low-Code Application Platforms | Microsoft Power Apps

• Low-code innovation using Power Platform – Microsoft Power Platform Blog

• Describe the business value of the Microsoft Power Platform – Training | Microsoft Learn

• App Building Software for Everyone | Microsoft Power Apps

By Nohelya Dos Santos

The post Low Code Dev Business Potential appeared first on Capitole.

For the International Women’s Day, we wanted to highlight one of the many women of our team, Irene Durán. Her story shows the achievements and challenges faced by women in technology, offering valuable insights into her journey as a QA engineer.

What motivated you to pursue a career in technology, specifically in QA?

Irene: Although it may sound cliché, I’ve been passionate about science and mathematics since I was young. The desire to understand the origin and functioning of everything around me led me to the great challenge of studying physics. I’ve always enjoyed challenges, and for me, the greatest challenge is being involved in solutions to everyday problems. Witnessing the explosion of the digital era has been fascinating, and ending up in the IT world has been the result. My dedication specifically to QA is due to both my thirst for knowledge and my obsessive drive to solve the errors I encounter.

As a QA tester and the QA community lead at Capitole, what are your main responsibilities and how do you tackle challenges in your daily work?

Irene: The responsibilities are varied: from test planning to automation and execution. For me, in product development, quality mostly resides in the process. If the path is good, so is the destination. In my day-to-day, I not only focus on ensuring that the product I validate meets quality requirements, but I also try to identify which process flows can be improved. Collaborating closely with different team members is challenging at times since testers are often seen as the villains and tend to be the last in the development chain. Constructive communication and the ability to adapt to timelines are key. As the community QA lead, my responsibilities are in themselves a major challenge: delivering current and interesting content for different profiles.

What has been your personal experience as a woman in the tech industry, and how do you think it has evolved over the years?

Irene: Paradoxically, I’ve encountered more difficulties during my student days than in my professional life. I was judged on several occasions for wanting to enter the notoriously called « men’s world, » putting all my effort into proving that I was capable of it. However, once I joined the workforce, I rarely experienced similar situations. My skills were valued, and I experienced an inclusive atmosphere. Throughout my short life, I’ve seen progress in ending that stigma in the industry, although there is still a long way to go.

Can you share a project or achievement that you are particularly proud of in your career as a QA tester?

Irene: My greatest achievement is more personal than professional. I’m quite proud of being very versatile: I’ve worked on projects in different industries, with various methodologies and technologies. When I started as a QA, I specialized in the automotive sector, and upon joining Capitole as a web application QA tester, my technical knowledge was much more limited than that of other colleagues. Perseverance and the desire for improvement have been key factors in catching up with colleagues who had been in the industry for years.

What is your vision for the future of women in the tech industry, especially in technical roles like yours?

Irene: As I mentioned before, there is still a long way to go. There is no future without knowing our past, filled with great female technology pioneers overlooked throughout history. Having visible role models is crucial for more women to feel encouraged to contribute to the sector. Currently, we are a pioneering generation in providing visibility and breaking stigmas. I see a lot of progress, and sincerely, I hope that gender will not be an obstacle to taking advantage of the future opportunities that this industry offers.

What advice would you give to young women considering a career in technology, especially in technical roles like QA?

Irene: Follow your passion and don’t pay attention to negative comments. Gender is not a limiting factor to pursue a technical role; success lies in the skills developed and the effort put into it. And if they are as meticulous as I am, envision a future as a QA

Irene Durán,
Senior QA Engineer and QA Community lead

The post Meet Irene Durán, QA Engineer at Capitole appeared first on Capitole.

Let’s imagine one situation together:

You’re leading a project for upgrading your company’s IT systems. You’re planning to proceed with an upgrade during the following weekend.

I’ll ask you now some questions:

• How would you guarantee all the key elements for ensuring a smooth deployment are in place?
• Are you sure you already communicated to all key project stakeholders?
• Did you assess all the risks related to your deployment?
• Who should be contacted in case of an incident? How?

During this post, we will review how to answer these questions with the Go/No-Go meeting.

At Capitole, we consider this is one of the critical steps to be taken before any deployment with impact.

Let’s go!

The meeting structure

There’re some points that must be present in every single Go/No-Go meeting. I’ll give a clear view of all of them.

I’ll base this explanation on the template I created some time ago and I use in all my meetings.

The decision tab
This tab is usually reviewed at the end of the meeting. We go through all the points we discussed with the stakeholders and agree on the final decision.
If there’s a conditional Go, we should register all the details in the « Additional comments » box.

Change description

This is the starting point of our meeting. We will start presenting the change.

The key information to share here would be:

• Change number (How could we find this change in the system?)
• Deployment Date (When are we planning to execute?)
• Project Manager ID (Who’s presenting the change?)
• Brief description of the work (High-level explanation of the scope)
• Justification (Why do we need to execute this change?)
• Impact (Locations and servers impacted)

After presenting this information to the audience, we should ask and solve all the questions they may have. It’s key they understand the scope and impact of the deployment.

Risks & mitigations

The Project Manager will assess all the risks in place and keep track of the status and mitigation actions for each one.

• Risk ID
• Risk description
• Date raised
• Risk owner
• Probability (Low, Medium, High)
• Impact (Low, Medium, High)
• Mitigation plan
• Status

We will share the risk list with the key stakeholders during the Go/No-Go meeting, so they know all the actions we’ve taken to manage them.

Contact Details

We will list all the people participating in the change execution here. We’ll include the information below for each contact:

• Contact name
• Contact method (mail/phone)
• Contact data
• Role

• Additional comments

Deployment and Communications plan

The Deployment Plan is a list of all the activities to be performed during the change. The rollback plan should also be included.

Communicating is a key activity in Project Management. The Communication Plan should include the points below:

• At what time are we communicating? Which deployment milestone do we want to communicate?
• Communication type. Are we sending an email or a message to a Teams group?
• Owner. Who is sending the communication?
• Distribution list. Who will receive the communication?

Conclusions

Today we reviewed together one of my day-to-day activities as a Project Manager. Following this Go/No-Go meeting approach, ensures you’ll reach success with your project deployments.

I would like to know how you’re preparing your project deployments. Are you scheduling the Go/No-Go meeting? I would love to read your comments below.

By Oscar Soto

The post Making or Breaking Projects: The Power of the Go/No-Go Meeting and How to Nail It appeared first on Capitole.

In the realm of software application development, QA analysts are akin to sentinels of success. When it comes to launching an app, the imperative is clear: it must operate with seamless perfection. In the crucible of app development, the art of streamlining user experiences becomes paramount. This involves a rigorous evaluation conducted by UI/UX experts and beta testers, identifying and resolving glitches at an early stage. By doing so, we ensure the birth of a successful, seamlessly functioning application, primed to make its mark in a competitive landscape.

Now, let’s turn our attention to the manifold benefits that software quality assurance brings to the table. Not only does it serve as a financial steward by nipping issues in the bud, minimizing overhead costs, and fostering an efficient development process, but it also takes on the mantle of enhancing the user experience. With eagle-eyed scrutiny, QA ensures that the app remains free of defects and operates in harmony with its intended design. This augmentation results in robust applications that adhere to stringent quality standards, minimizing the risk of system crashes, data breaches, or data corruption. Additionally, QA’s vigilance extends to the realm of security, where it detects and eliminates vulnerabilities, safeguarding sensitive user data and fostering trust among clients. Furthermore, QA adapts to the ever-evolving landscape of software development, aligning itself with the shifting sands of methodologies such as agile and DevOps.

It encompasses various types of testing, including functional, usability, performance, and security testing, with each type serving as a building block for overall software quality. Automation in QA accelerates the process and ensures consistent testing, although it comes with its own set of challenges. The human element in QA is pivotal, as skilled analysts not only detect bugs but also bring a critical perspective, offer valuable feedback, and contribute to overall product quality. By prioritizing quality assurance, we not only fulfill our clients’ expectations but also cultivate trust and loyalty. The judicious use of advanced tools and automation serves as the crowning touch, empowering us to optimize efficiency, curtail costs, and deliver consistently high-quality projects.

By Kevin Redlich

The post The Crucial Role of Quality Assurance in Software Development appeared first on Capitole.

Introduction

Playwright, an open-source library by Microsoft, stands out in E2E testing for its compatibility with multiple browsers and programming languages, offering fast and efficient automation.

Why Playwright?

Playwright is an open-source library developed by Microsoft that has gained significant popularity in the world of E2E testing. One of the primary reasons for discussing Playwright is its versatility and efficiency. Playwright allows for E2E testing in multiple browsers, including Chrome, Firefox, Edge, and Safari. Furthermore, it is compatible with various programming languages, making it accessible to a wide range of developers. Its user-friendly nature and integration with popular testing frameworks like Jest make it an attractive choice.

Playwright offers several key advantages:

Playwright is versatile, supporting tests in Chrome, Firefox, Edge, and Safari. Its integration with frameworks like Jest makes it appealing. It offers multi-browser support, compatibility with several languages, and an intuitive API for rapid automation.

Comparison with Some Existing Frameworks

Special Features of Playwright

Playwright also offers a set of advanced features that set it apart:

Resilience: Tests in Playwright are resilient to instability. The tool eliminates the need for artificial wait times, which are a major cause of unstable tests. Assertions are automatically retried until the necessary conditions are met, ensuring consistent results.

Tracing: Playwright allows for the configuration of retry strategies and captures execution traces, videos, and screenshots to eliminate instability issues, facilitating problem debugging.

No Limitations: Playwright leverages the modern architecture of browsers and runs tests outside the browser process, eliminating typical limitations of in-process test runners. This enables the execution of scenarios spanning multiple tabs, multiple origins, and multiple users in a single test.

Reliable events: Playwright employs a reliable event system that allows interaction with dynamic elements and the emulation of real user events.

Complete isolation: Each test in Playwright runs in an isolated browser context, ensuring complete separation and zero interference between tests. It also allows for the preservation of authentication state for reuse across all tests, avoiding repetitive login operations.

In summary, Playwright offers advanced features that ensure resilience, the elimination of unstable tests, the ability to execute complex scenarios, and a high degree of isolation, resulting in fast and efficient execution of E2E tests. These features make it a solid choice for projects requiring high-quality and reliable testing.

The world of software is constantly changing and advancing, and at Capitole, we are always at the forefront of these trends to provide our clients with the most innovative and effective solutions.

Jose Alberto Lorenzo

The post Playwright E2E Testing for Modern Applications appeared first on Capitole.