Operations | Monitoring | ITSM | DevOps | Cloud

Have you ever asked an AI model a question and received an outdated or completely off-base response? I’ve been there too. The problem is that most AI models rely solely on their pre-trained knowledge, which becomes obsolete over time. This is where RAG can help: RAG is a hybrid AI technique that combines the advantages of retrieval systems and generative models. It bridges the gap by bringing in real-time information from external knowledge sources to improve the generation quality.

Development teams managing Kubernetes-based applications face challenges in maintaining visibility and control over their deployment processes. Without a centralized interface, teams struggle to track, monitor, and manage releases across their Kubernetes clusters, leading to potential deployment errors, and difficulties in maintaining consistent deployment workflows.

The pace of software development has changed. With AI coding assistants now embedded into engineering workflows, developers are building faster, shipping sooner, and writing more code than ever before. But as velocity increases, so does the complexity of keeping that code running. When builds fail, developers need answers fast. They need clarity, context, and actionable feedback right where they’re working.

Penetration testing is a type of software testing where skilled security professionals attempt to breach an organization’s systems and networks to identify vulnerabilities before malicious actors can exploit them.

The way software is written is changing fast. In the past few years, AI coding assistants and large language models (LLMs) have gone from novelty to necessity for many developers. Tools like Cursor, ChatGPT, and custom in-house models are helping teams generate boilerplate, scaffold features, and even build entire apps within minutes. It’s exciting. But it also raises the stakes. When code is written faster, it’s deployed faster.

A benchmark can be interpreted as a standard of comparison used to assess something. In everyday life, for example, when we want to buy a new cellphone and want to know which one is faster, we can see the speed test (benchmark) by measuring how fast the cellphone opens applications or runs games. From there, we can compare which cellphone is better based on the numbers produced.

As microservices architectures dominate modern application development, the ability to test, secure, and automate their deployment has become a vital skill. In this guide, you’ll learn how to: Let’s first set the stage by briefly exploring the foundational concepts of CI/CD and DevOps, which underpin the automation and agility required in development workflows.

Argo CD is a declarative continuous delivery (CD) tool for Kubernetes. Argo CD pulls Kubernetes configurations (such as manifests, Helm charts, and Kustomize overlays) from a Git repository and applies them to a Kubernetes cluster. With Argo CD, developers can automatically deploy changes to their Kubernetes environments by updating their Git repository. Argo CD continuously monitors Kubernetes deployments and ensures their state matches the configuration declared in Git.

Machine learning (ML) and artificial intelligence (AI) are often used interchangeably in tech discussions, yet they represent distinct concepts with important differences. While AI refers to the broader field of creating machines capable of intelligent behavior that mimics human capabilities, machine learning is a specific subset of AI focused on developing algorithms that allow computers to learn from and make predictions based on data.

As AI becomes increasingly central to modern software development, the ability to craft effective system prompts has emerged as a crucial skill. Whether you’re building a code generation tool, creating a chatbot, or developing AI-powered features, your success largely depends on how well you can communicate with AI models through prompts. At CircleCI, we’ve spent countless hours working with developers who are integrating AI into their applications.