Day: November 17, 2023

In the ever-evolving technology landscape, businesses are increasingly recognizing the need to modernize their applications and make the leap to the cloud. This strategic move is not just a trend but a pivotal decision to enhance agility, reduce operational costs, and unlock the full potential of advanced cloud services. Let's embark on a comprehensive exploration of the application modernization roadmap, delving into the intricacies of a swift and secure transition to the cloud.

The Essence of Application Modernization

As businesses evolve, so do their technological needs. Legacy systems that were once the backbone of operations can become impediments to progress. Application modernization is the strategic process of revitalizing existing applications, making them more efficient, scalable, and aligned with contemporary business requirements.

Prompt engineering is a vital aspect of leveraging the full potential of AI language models. By refining and optimizing the instructions given to these models, we can obtain more accurate and contextually relevant responses. In this article, we explore the principles and techniques of prompt engineering, along with its limitations and potential applications.

Principles of Prompt Engineering

1. Writing Clear and Specific Instructions

The success of prompt engineering begins with providing clear and unambiguous instructions. Clear doesn’t necessarily mean a short description. Being specific about the desired output helps the model understand the task more accurately. For example, tell the LLA to be an expert in the field you are asking for.

Kubernetes (K8s), an open-source container orchestration system, has become the de-facto standard for running containerized workloads thanks to its scalability and resilience.

Although K8s has the capabilities to streamline deployment processes, the actual deployment of applications can be cumbersome, since deploying an app to a K8s cluster typically involves managing multiple K8s manifests (like Deployment, Service, ConfigMap, Secret, Ingress, etc.) in YAML format. This isn't ideal because it introduces additional operational overhead due to the increased number of files for one app. Moreover, it often leads to duplicated, copy-pasted sections of the same app across different environments, making it more susceptible to human errors.

LinkedIn's feed has come a long way since the early days of assembling the machine-learning infrastructure that powers it. Recently, a major update to this infrastructure was released. We caught up with the people behind it to discuss how the principle of being people-centric translates to technical terms and implementation. 

Introduction

How do data and machine learning-powered algorithms work to control newsfeeds and spread stories? How much of that is automated, how much should you be able to understand and control, and where is it all headed? 

The hype around LLMs is unprecedented, but it’s warranted. From AI-generated images of the Pope in head-to-toe Balenciaga to customer support agents without pulses, generative AI has the potential to transform society as we know it. 

And in many ways, LLMs are going to make data engineers more valuable — and that’s exciting!

Secure payment processing is vital for ensuring customers can shop safely on your app. Cyberattacks become more frequent each year, with a particular emphasis on stealing financial information. Luckily, you can implement a few best practices to simplify security and protect your clients’ data. 

1. Use Multifactor Authentication

Multifactor authentication (MFA) is one of the top methods for securing payment systems today. It involves verifying the customer’s identity using a secondary confirmation method. For example, people can verify a legitimate payment attempt by entering a one-time code sent to their verified phone number. 

Smoke testing, often referred to as "build verification testing" or "sanity testing," is a powerful tool that brings unique advantages to software development teams. It gives confidence that critical functionalities behave as expected and that code stability can be maintained through issue resolution from fast feedback mechanisms.

Smoke Testing vs. Regression Testing

Smoke testing is a subset of regression testing. Take a regression test suite and extract the absolutely necessary tests that you need for your verifications and your validations. Extract the most critical tests - the ones that if they fail testing then the bugs that have just been uncovered must be fixed immediately. You've got yourself a smoke test suite now! Maybe you plan to release a new feature and you want to conduct smoke testing early for quick feedback. Or maybe you performed a bug fix, performance improvements, or code restructuring, and you want a quick idea if your system was negatively impacted in a big way. Smoke testing is what you need.

Event-driven architectures have been successfully used for quite an amount of time by a lot of organizations in various business cases. They excel at performance, scalability, evolvability, and fault tolerance, providing a good level of abstraction and elasticity. These strengths made them good choices when applications needed real or near real-time reactiveness.

In terms of implementations, for standard messaging, ActiveMQ and RabbitMQ are good candidates, while for data streaming, platforms such as Apache Kafka and Redpanda are more suitable. Usually, when developers and architects need to opt for either one of these two directions they analyze and weigh from a bunch of angles – message payload, flow and usage of data, throughput, and solution topology. As the discussion around these aspects can get too big and complex, it is not going to be refined as part of this article.

Conversational interaction with large language model (LLM) based solutions (for example, a chatbot) is quite common. Although production grade LLMs are trained using a huge corpus of data, their knowledge base is inherently limited to the information present in the training data, and they may not possess real-time or the most up-to-date knowledge.

Here is an example:

Enterprises must leverage technology's potential to streamline their processes, enhance customer interactions, and foster innovation to remain competitive. It's an established truth that an organization's triumph is contingent on its capacity to smoothly amalgamate systems, applications, and data. Among the most revolutionary approaches in this context is API-driven integration, acting as the linchpin of contemporary business functions. As an integration evangelist with extensive and diverse experience, I hold a steadfast belief that API-driven integration is not merely a passing trend; it's a transformative force poised to reshape how businesses function, excel, and evolve.

The widely discussed approach of API-led integration represents a purposeful and strategic method for unifying and aligning diverse systems, applications, and data sources within an organization. It essentially revolves around the seamless connection of various software components, utilizing Application Programming Interfaces (APIs) as foundational building blocks. These APIs facilitate effective and standardized data exchange and communication among different applications, acting as intermediaries.

I bet every single entrepreneur has scalability on the list as they plan their future app. No matter the business goals in mind, everyone would be happy to get a stable app that survives Black Friday without a hint of a glitch. 

Hey, I’m Alex Shumski, Head of Presales at Symfa. So far as building software architectures is what I have been doing for a living for years and years, I’m here to suggest a thing or two to those who aren’t certain if they need app scaling at all and which strategy to follow, if any.

Large Language Models are advanced AI systems that can answer a wide range of questions. Although they provide informative responses on topics they know, they are not always accurate on unfamiliar topics. This phenomenon is known as hallucination.

What Is Hallucination?

Before we look at an example of an LLM hallucination, let's consider a definition of the term "hallucination" as described by Wikipedia.com(opens a new window: