Artificial Intelligence

Strategic operational models for maximizing GenAI in financial services

Leave a Comment / Artificial Intelligence / Rafaela

Generative artificial intelligence (GenAI) is transforming the banking sector, bringing new opportunities for innovation and efficiency. With the promise of adding between 200 and 340 billion dollars to the annual value of the global industry, the strategic implementation of this technology could transform the way banks and financial institutions operate. However, choosing the right operating model is critical to making the most of this technology. In this article, we’ll explore the best practices for selecting and implementing the ideal Generative AI operating model for your business. Understanding the operating model for GenAI An operating model refers to the way a company structures and manages the integration of technology into its operations. The choice of this model is crucial, as it directly affects the efficiency and success of the implementation of Generative AI. A well-selected model can guarantee: Operational efficiency: a well-structured model ensures the efficient allocation of resources and effective coordination between different departments and systems within the organization. Flexibility and adaptation: the flexibility of the operating model is essential to ensure that AI will adapt to technological changes and new market demands. Risk management: a suitable operating model helps to minimize the risks associated with adopting Generative AI, such as integration failures and security issues. Operational models for implementing Generative AI in the financial sector The model adopted by a business can vary from a centralized approach, where one department controls Generative AI, to a fully decentralized model, in which different areas of the company have the autonomy to do so. Highly centralized model: In this model, the management and coordination of Generative AI is centralized in a specific team, offering control and consistency. This approach allows for the uniform development of skills and the definition of clear guidelines. However, there can be a disconnect with the business units, which can make it difficult to integrate the technology with the specific needs of each area. Centrally led model, executed by business units: Here, the centralized GenAI team leads strategy and development, while the business units are responsible for executing the solutions. This model facilitates integration and support throughout the company, promoting closer collaboration between the parties involved. However, the need for approval from the business units can result in delays in implementing the technology. Business unit-led model with central support: In this model, the business units lead the implementation of Generative AI with centralized support for resources and guidelines. This facilitates the adoption of the technology and aligns the solutions to local needs. However, coordination between different units can be challenging, and there can be variations in the development and application of the technology between the different areas. Highly decentralized model: Each business unit or department is responsible for its own Generative AI initiatives. This model offers great flexibility and customization, allowing each area to adapt the technology to its specific needs. However, there can be challenges related to integration and coordination between the different systems and processes, as well as a possible lack of access to best practices and centralized knowledge. Each approach has different benefits and challenges. However, in the financial sector, most institutions prefer a centralized model, as studies show that 70% of companies that have adopted this model have advanced in their use of technology, compared to only 30% of those that have opted for a fully decentralized model. Criteria for selecting and evaluating the GenAI operating model Choosing and implementing a Generative AI operating model for banks and fintech requires a careful analysis of several areas, taking into account internal and external aspects, such as: 1. Alignment with strategic objectives Definition of goals: Establish clear objectives for the implementation of AI, such as improving operational efficiency, developing new financial products, or innovating existing processes. Needs analysis: Identify the organization’s specific needs in terms of the resources, technology, and capabilities required for successful implementation. 2. Assessment of the operating model’s capacity Resources required: Assess whether the model can support the scale and complexity of implementing Generative AI. This includes the availability of specialized talent, adequate technological infrastructure, and necessary data. Flexibility and scalability: The model must allow for adjustments and expansions as the technology and the organization’s needs evolve. The ability to integrate new functionalities and adapt to market changes is essential. 3. Integration and compatibility Compatibility with legacy systems: Check that the GenAI technology is compatible with the organization’s existing systems. It may be necessary to update or adapt old systems to ensure efficient integration. Interoperability: Ensure that Generative AI can interact and communicate effectively with other technologies and platforms. Developing interfaces and integration protocols may be necessary to ensure smooth operation. 4. Security and privacy Data protection: Implement strict measures to protect data from unauthorized access and leakage. Use advanced encryption, strict access controls, and frequent audits to ensure data integrity. Regulatory compliance: Make sure your AI implementation complies with privacy and data protection regulations, such as the GDPR. Compliance is essential to avoid penalties and maintain customer trust. 5. Talent management and training Recruitment and retention of experts: Attract and retain highly qualified professionals in GenAI and data science. Collaborating with academic institutions and investing in continuing education programs can help ensure the availability of specialized talent. Continuous development: Promote the continuous development of staff skills to keep up to date with the latest innovations and best practices in AI. Training and certification programs are key to preparing staff for new challenges. 6. Evaluation and continuous adjustment Monitoring and measuring performance: Establish metrics and performance indicators to evaluate the effectiveness of Generative AI. Use this information to identify areas for improvement and adjust solutions as necessary. Feedback and iteration: Collect feedback from users and stakeholders to continually refine AI solutions. Creating feedback channels and carrying out periodic reviews are crucial to ensuring the ongoing relevance and effectiveness of the technology. Ensuring the success of Generative AI in the financial sector To maximize the potential of this technology, financial institutions must consider the pace of innovation, their organizational culture,

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AI illustration that reminds us of the importance of AI for Large Enterprises.

Scaling AI for Large Enterprises: Overcoming Integration Challenges

Leave a Comment / Artificial Intelligence / Júlia Ilkiu

AI for Large Enterprises is already a reality in the business world, transforming how organizations make decisions, streamline operations, and gain valuable insights from their data. However, when it comes to scaling AI from pilot projects to full organizational deployment, AI for Large Enterprises often faces unique challenges. These include integrating with legacy systems, dealing with fragmented data, and managing complex operational structures. In this article, we’ll explore the key challenges AI for Large Enterprises face when scaling, and offer practical strategies for overcoming them. Whether you’re just starting your AI journey or looking to expand existing projects, this guide will provide useful insights into building scalable and future-proof AI solutions. The potential of AI for large enterprises AI for Large Enterprises offers the opportunity to transform operations by increasing productivity, creating new business models, and improving customer experiences. The value of AI lies not only in its ability to quickly process vast amounts of data but also in how it helps businesses make informed decisions, automate processes, and make accurate predictions. For large enterprises, AI can automate routine tasks, provide predictive analytics for better decision-making, and optimize operational processes. Organizations using AI for Large Enterprises in predictive analytics can anticipate changes in customer behavior, predict machine breakdowns, and detect financial fraud with unparalleled accuracy. Despite this potential, many organizations find that scaling AI for Large Enterprises from small-scale experiments to enterprise-wide deployment is more challenging than anticipated. While pilots may be successful, scaling these solutions reveals complex issues that need to be addressed. Key challenges in scaling AI Infrastructure limitations One of the biggest barriers to scaling AI is the existing IT infrastructure. Many organizations rely on legacy systems that were not designed to handle the computational demands of AI, such as real-time data analysis and large-scale storage. AI solutions that use deep learning, for example, require significant computing power, such as GPUs or TPUs, which legacy infrastructure often cannot support. Solution: Organizations can mitigate this problem by investing in cloud-based solutions that offer flexibility and scalability. Cloud platforms allow organizations to expand their computing resources as needed, without the high cost of upgrading physical hardware. This approach ensures that organizations can scale AI without breaking their budget. Data management and integration The success of AI depends on access to large volumes of high-quality data, but many organizations struggle with fragmented and inconsistent data stored in departmental silos. In addition, integrating AI with existing enterprise systems can be complicated, often requiring data format conversions and compatibility between old and new technologies. Solution: Establishing strong data governance is key to creating a solid foundation for AI. Data integration, standardization, and validation processes are essential to ensure that AI models are trained on accurate and reliable data. Integration tools that consolidate data from multiple sources can improve the efficiency of AI models and enhance results. Cultural resistance The implementation of AI can sometimes be met with internal resistance, especially when it involves significant changes to workflows and employee roles. Employees may fear that AI will replace their jobs or drastically alter their responsibilities. This type of resistance can slow down the widespread adoption of AI. Solution: Overcoming cultural resistance requires investment in education and training programs. Showing how AI can complement human work, rather than replace it, can help create a more open attitude towards innovation. In addition, leadership advocacy is critical to fostering a culture of innovation and building trust in the use of new technologies. Skills gaps Scaling AI requires specialized skills, such as data science, machine learning, and AI expertise. However, many companies struggle to find and retain qualified professionals in these areas. A shortage of AI experts can slow down projects and undermine the quality of the solutions developed. Solution: To address this talent shortage, companies should invest in ongoing training programs for their employees. This can include building multidisciplinary teams that bring together data scientists, AI engineers, and business leaders to ensure that AI projects are aligned with business goals. Partnering with external vendors can also help fill talent gaps and accelerate the scalability process. Cost and resource allocation Scaling AI can be expensive, requiring significant investments in infrastructure, technology, and talent acquisition. Without proper planning, these costs can easily exceed the budget, making it difficult to achieve a return on investment. Solution: An incremental approach to AI implementation is an effective way to manage costs. By starting with high-impact areas, organizations can quickly demonstrate the value of AI and use these results to fund future expansions. In addition, using cloud-based AI services can help control costs by offering a pay-as-you-go model, reducing the need for large upfront investments. The 5 best practices for scaling AI 1. Focus on high-impact pilots Before scaling AI across the organization, it’s important to validate its effectiveness through pilot projects in high-impact areas, such as customer service automation or supply chain optimization. These projects provide tangible results that can form the basis of a broader expansion plan, as well as secure buy-in from the organization’s leadership. 2. Invest in scalable infrastructure The success of AI depends on having an infrastructure that can handle its demands. Cloud platforms, modular architectures, and hybrid solutions provide the flexibility and scalability needed to support the growth of AI. With scalable infrastructure, organizations can ensure that their AI initiatives can adapt to future expansion without requiring major technology overhauls. 3. Encourage collaboration between teams Scaling AI requires close collaboration between technical and business teams. Multidisciplinary teams consisting of data scientists, software engineers, and business leaders are essential to ensure that AI solutions are not only technically sound but also aligned with business objectives. 4. Continuous improvement and iteration AI models require constant monitoring, refinement, and retraining to remain effective and accurate. By adopting an agile approach to AI development and implementation, organizations can respond quickly to market changes and adjust their models based on ongoing feedback. 5. Ethics and transparency As AI plays a greater role in decision-making processes, ensuring transparency and ethical use

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The power of Generative AI to create personalized financial products

Generative Artificial Intelligence (Generative AI) is redefining the financial sector, offering an innovative approach to understanding and meeting customer needs. In a scenario where personalization is becoming increasingly essential, GenAI is redefining the role of financial institutions, enabling them to create financial products and services that are highly tailored to individual needs. As technology continues to shape the future of business and the global economy, Generative AI stands out as one of the most promising innovations. The technology makes it possible to create precisely tailored financial solutions and promises to accelerate significant changes in the sector. According to Gartner, 80% of CFOs plan to increase their investments in AI over the next two years, reflecting growing confidence in the potential of this technology. What is Generative AI? Generative AI goes beyond traditional artificial intelligence, using advanced machine learning techniques to create entirely new solutions from raw data. This includes everything from investment portfolios to personalized insurance, all tailored to the unique needs of each client. In the financial sector, this means that AI can generate financial products ranging from personalized investment portfolios to tailor-made insurance and retirement plans. In the financial world, Generative AI is being used to understand the best profile for each client, customizing products and services according to individual needs. For example, a bank can use Generative AI to analyze customers’ spending patterns and predict their future financial behavior. This allows bank managers to offer personalized financial advice and suggest products that align with each customer’s goals and preferences. Traditional AI vs Generative AI Traditional AI, or prescriptive AI, has been widely used to assess financial risks, automate processes, and analyze large volumes of data in search of patterns and trends. However, this form of AI is limited to performing a single specific task, requiring considerable time and resources for training. Although effective in its functions, prescriptive AI does not have the flexibility or adaptability needed to cope with the complexities and rapidly evolving demands of the financial market. The real revolution comes with Generative AI and big language models, which are transforming sectors where the use of data, language, and images is central, as noted by Harvard Business Review in the so-called WINS Work sectors. GenAI enables deeper and more dynamic integration in financial operations, from the front office, increasing liquidity, to the automation of tasks in the back office. With its ability to analyze and understand data in real-time, Generative AI offers mass customization, precisely tailoring financial products to individual customer needs, while making processes more efficient and scalable. Benefits of AI in Financial Product Development Generative AI is not just transforming operations; it’s redefining the entire customer experience. Imagine a future where every financial product is custom-tailored to fit your life goals seamlessly—this is the new reality that AI brings. By leveraging vast amounts of data and advanced predictive algorithms, AI enables financial institutions to craft products and services perfectly aligned with each customer’s unique needs. Among the key benefits of using Generative AI in the development of financial products are: Efficiency and Scalability Generative AI empowers financial institutions to deliver highly personalized solutions at scale, which would be impossible with traditional methods. It can analyze a customer’s transaction history, spending patterns, and financial goals to suggest the most appropriate products, such as recommending a migration to a better-suited bank account plan or proposing a personalized investment portfolio. By streamlining these processes, AI reduces operational costs and accelerates product development, boosting overall efficiency. Data-Driven Decision-Making AI excels at analyzing vast volumes of data in real time with speed and precision, allowing institutions to make more informed and timely decisions. For example, by combining data from various customer touchpoints, AI can predict when a customer might benefit from an updated credit card plan or a tailored loan offer. This capability is precious in volatile economic environments, where being agile and accurate in decision-making is critical. Risk Reduction By identifying patterns and predicting customer behavior, AI plays a crucial role in mitigating financial risks. It can automatically adjust products and strategies based on evolving market conditions or changes in a customer’s financial profile. For instance, if AI detects an increase in a customer’s financial risk, it could proactively suggest a shift to more conservative investment options or recommend insurance products that better match their current needs. Fraud Detection and Security AI systems enhance security by monitoring transactions in real time, identifying suspicious activities, and preventing fraud before it impacts customers or institutions. This continuous monitoring not only protects against financial losses but also strengthens customer trust by ensuring their assets and data are secure. Enhanced Customer Experience By providing products that are meticulously tailored to individual needs and ensuring faster, more efficient service, AI significantly enhances the customer experience. This personalized approach increases customer satisfaction and loyalty, as clients receive financial advice and products that are not only relevant but also aligned with their financial journey. Challenges of using Generative AI Generative AI, with all its potential, requires financial institutions not only to adapt but also to lead the way in innovation and data security. One of the main obstacles is the effective management of huge volumes of sensitive information. Securely integrating this data into AI systems requires a robust infrastructure and strict governance practices. In addition, it is crucial to guarantee the quality and accuracy of the data used to avoid bias and ensure that the financial products generated are reliable and effective. Another significant challenge is regulatory compliance and cyber security. The financial sector operates under strict regulations such as GDPR, LGPD, and CCPA, which require extreme care in protecting customer data. The introduction of more complex AI systems amplifies cybersecurity risks, requiring substantial investments in protection and monitoring. Overcoming these challenges is not only a necessity but an opportunity for financial institutions to position themselves at the forefront of innovation, setting new standards of excellence and trust in the market. The Future of Generative AI in the Financial Sector The future of

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The role of Prompt Engineering in the age of AI

Leave a Comment / Artificial Intelligence / Júlia Ilkiu

Prompt engineering is an emerging field that is revolutionizing how we interact with artificial intelligence (AI). By combining technical expertise with a deep understanding of human language, prompt engineers bridge the complexity of machine learning algorithms with the simplicity of human communication. In this article, we will explore the fundamental elements for success in prompt engineering and how this emerging demand shapes the future of technology work. Impact of predictive technologies on software quality The integration of predictive technologies into software development is significantly changing the way products are conceived, developed, and brought to market. According to a McKinsey report, companies that incorporate advanced algorithms into their operations experience an average 20% increase in developer productivity. These predictive models are revolutionizing the software development process by automating repetitive and complex tasks, allowing developers to focus on high-level strategy and solving more complex problems. Studies conducted by MIT support this trend, indicating that the use of predictive technologies can reduce product launch times by up to 30%. In addition, AI is raising software quality standards through automated testing and intelligent debugging techniques, solidifying predictive technologies as an essential component of the future of software engineering. I see prompt engineering as a fundamental innovation in human interaction and algorithmic systems. The ability to provide precise and contextually relevant instructions to predictive models not only optimizes the efficiency of these systems but also paves the way for the development of more sophisticated and intuitive technological solutions. This is an emerging field that will undoubtedly continue to evolve and play a critical role in digital transformation. As CTO, I have watched the integration of generative AI (GenAI) into software development change the way we build and improve systems. It increases the productivity and creativity of our team. With AI, it is possible to update large amounts of legacy code to modern languages, rewrite code, and write new functionality. And that is just the beginning! The concept of Prompt Engineering Prompt Engineering is a method for training artificial intelligence. Using commands, instructions, and context, a prompt engineer defines the parameters within which the AI will operate to generate accurate and appropriate responses. At its core, prompt engineering involves creating and optimizing commands, or “prompts,” that guide AI models to perform specific tasks. Think of how you would teach your pet a new trick: you would provide clear and direct instructions to guide it. Similarly, prompts serve as detailed instructions that help AI models understand what is expected of them and ensure that their responses are accurate and relevant. – While creating prompts may seem simple, the true complexity lies in getting the AI to understand context and nuance the way humans do. This requires a deep understanding of machine learning principles and human language constructs. For example, if we want an AI model to generate dessert recipes, a vague prompt like “Create a recipe” might yield irrelevant responses. In contrast, a more specific prompt such as “Create a chocolate dessert recipe” will steer the model toward a more appropriate outcome. The science behind prompts Prompting is a science in itself. It requires specialized skills in software development, AI, and machine learning. With the increasing use of AI technologies across industries, prompt engineering is quickly becoming a high-value career, with skilled professionals commanding significant salaries. The evolution of prompt engineering reflects our growing understanding of AI. Initially, simple rule-based systems were the norm, but as machine learning models have become more complex, the need for carefully crafted prompts has become apparent. The quality of the prompts directly affects the quality of the responses generated by the AI models. An example of this is the development of GPT-4 by OpenAI. This language model can generate coherent and contextually relevant text based on specific prompts. However, even with a large number of parameters, precise prompts are necessary to achieve the desired results. A vague prompt can lead to varied and out-of-context responses. Precision and clarity in prompts are critical to guiding AI correctly. Prompt engineering process Through carefully crafted prompts, we guide linguistic models to produce relevant, informative, and creative responses. There are three stages to this process: Task Definition and General Setup: the first step is to set clear and precise goals for interacting with the model. This includes defining the specific task objective and setting parameters such as batch size, temperature, and learning rate. Properly configuring the development environment and selecting the right hyperparameters are essential steps to ensure that the model performs as expected. Prompt Creation: with goals and parameters defined, the next step is to create the initial prompt for the model. This prompt should be clear, concise, and informative, providing the necessary context and using natural language. The prompt must be specific enough to avoid ambiguity and guide the model in the desired direction. Refinement and iteration: the ideal prompt are rarely found on the first try. Therefore, the third stage involves an ongoing process of refinement and iteration. This involves analyzing the model’s responses and adjusting the original prompt as needed. Each iteration brings the prompt closer to perfection, ensuring that the final result meets predefined expectations and requirements. Prompt engineering applied to the market As AI continues to infiltrate industries ranging from healthcare to finance, the need for customized prompts is growing. In customer service chatbots, for example, well-crafted prompts are essential to avoid frustration and ensure that responses are helpful and accurate. A chatbot that receives a poorly crafted prompt may respond with irrelevant information, whereas an optimized prompt ensures that the customer’s request is understood and answered correctly. Requirements for becoming a Prompt Engineer Prompt engineering is a multidisciplinary field. While there are specific courses and certifications, several areas of study can provide a solid foundation for this career, including computer science, data science, linguistics, and even psychology. Computer Science and Programming: a solid understanding of computer science and programming languages such as Python and Java is essential. These skills are essential for building and fine-tuning AI-based systems.

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A step-by-step guide to building an AI system

Leave a Comment / Artificial Intelligence / Júlia Ilkiu

Artificial Intelligence (AI) is having a profound impact on the direction of business and society as a whole. Its ability to create autonomous systems capable of performing complex tasks is redefining the limits of what was previously thought impossible. And the best news is that building an AI system is no longer an intimidating or expert-only process, but is within the reach of many. From AIs writing articles about themselves to AIs winning art competitions, the limits of autonomous systems are being challenged and expanded every day. This inspiring scenario makes many people curious about how to build their own AI systems and wonder if this complex technology is within the reach of ordinary people. The answer is yes! While building an AI system from scratch can be a complex challenge requiring advanced technical expertise, there are several tools available to facilitate the process. Both commercial and open-source solutions offer user-friendly features and interfaces that allow beginners, even those with no prior programming experience, to take their first steps into the fascinating world of AI. This article serves as a practical guide to the process of building an AI system, opening the door for you to explore the fascinating world of AI system with complete confidence. Prepare to embark on a journey of learning and discovery where you can master the basics of AI system. Programming Languages and AI Before we dive into the stages of building an AI system, it’s important to understand the programming languages that are best suited for the job. While any robust language can be used, some stand out in the context of AI, here are a few: Python This general-purpose language is a popular choice because of its readability and the wide variety of libraries available. Python is particularly well suited to AI, with frameworks such as PyTorch simplifying the development process. But what makes Python so great for AI? Simplicity and readability: Python’s intuitive syntax makes it easy to learn and write code, even for beginners. This allows you to focus on AI concepts rather than the complexities of the language. Versatility: Python is a general-purpose language, which means it can be used for a wide range of tasks, from data analysis and web development to, of course, AI. This versatility makes it a valuable tool for any professional in the field. Rich libraries and tools: The Python universe offers a vast set of AI-specific libraries and frameworks such as NumPy, Pandas, TensorFlow, and PyTorch. These tools facilitate the development of machine learning, natural language processing, and computer vision models, accelerating your development process. Vibrant community: The Python community is extremely active and engaged, with numerous online forums, tutorials, and support groups to help beginners and experts alike. This community ensures that you will always have access to valuable help and resources on your learning journey. Julia A newer language, Julia was designed specifically for scientific computing and data analysis. Its streamlined syntax and impressive performance make it an attractive option for AI projects. Less syntactic complexity: Compared to languages like Java or C++, Julia presents a more intuitive and less complex syntax, making it easier for beginners to learn and write code. Superior performance: Julia excels in processing speed, outperforming languages like Python or R, making it ideal for efficiently handling large datasets and complex algorithms. Designed for data science: Unlike other general-purpose languages, Julia was designed specifically to meet the needs of data science. This means it has native features and functionality that make it easy to work with data, from collection and preprocessing to analysis and visualization. R Although it has been eclipsed in popularity by Python, R remains a solid choice, especially for statistical tasks and data analysis. Its large collection of packages makes it a valuable tool for data scientists. Although its syntax can be challenging for beginners, R offers a vast universe of libraries that specialize in various areas of data science, such as Statistical analysis: A complete set of tools for performing complex statistical analysis, from hypothesis testing to linear regression and nonlinear modeling. Data Processing: Robust libraries for manipulating, cleaning, and preparing large data sets for analysis. Data Visualization: Powerful tools for creating meaningful graphs and visualizations that help you understand your data. What are the steps in building an AI system? Now that we understand the tools at our disposal, let’s dive into the practical steps of building an AI system. 1. Set a goal Before you start writing code, it’s important to clearly define the problem your AI system will solve. The more precise your goal, the more effective your solution will be. Determine the value proposition of your product and why investing in it is a smart decision. 2. Collect and clean data As the saying goes, “garbage gets in, garbage gets out.” Data quality is critical to the success of an AI project. Make sure you collect relevant, unbiased data and spend time cleaning and organizing it. In the AI universe, data can be divided into two main types: Structured data, which is organized in a defined format, such as spreadsheets, relational databases, or CSV files. Unstructured data is not organized in a formal format, such as free text, images, audio, or video. What makes data “right” for AI? Relevance: The data must be directly related to the problem the AI model is trying to solve. This means that the data must contain the necessary information for the model to learn and make accurate predictions. Adequacy: The amount of data should be sufficient to adequately represent all variables and nuances of the problem. A trained model with insufficient data can lead to incorrect generalizations and inaccurate results. Impartiality: Data should not contain biases or distortions that could lead the AI model to make unfair or discriminatory decisions. It is critical to ensure that data is collected and pre-processed impartially to avoid algorithmic bias. 3. Create the algorithm There are several techniques and algorithms available for building an AI system, from

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Strategic operational models for maximizing GenAI in financial services

Scaling AI for Large Enterprises: Overcoming Integration Challenges

The power of Generative AI to create personalized financial products

The role of Prompt Engineering in the age of AI

A step-by-step guide to building an AI system

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