Compared to traditional programming, where code compiles into deterministic logic, prompt engineering is a form of controlled ambiguity. It’s an interface where human language meets machine probability. So, how does prompt engineering differ from traditional programming? The differences are technical, behavioral, and philosophical. In this blog, we’ll break them down and also cover the tools, skills, and more.

Prompt engineering has quickly evolved from an experimental curiosity into a core skill driving some of today’s most powerful AI systems. In this blog, we’ll walk through a structured, practical path to entering the prompt engineering space.

Prompt engineering is no longer a side skill but a core part of how modern developers build applications using large language models (LLMs). While the mechanics of writing a prompt seem simple, real-world usage quickly reveals recurring pain points that affect accuracy, reliability, scalability, and user experience.

As large language models (LLMs) like GPT-4, Claude 3, Gemini, and open-source alternatives become foundational in modern development workflows, prompt engineering has emerged as a core competency for software engineers, product teams, and AI practitioners. This blog covers the key categories of tools that support prompt engineering and offers practical recommendations for choosing the right stack for your workflow.

Prompt engineering used to be a niche. Now it’s a core developer skill. As large language models (LLMs) like GPT-4, Claude, and Gemini grow more powerful, the ability to communicate with them effectively, through well-crafted prompts, has evolved from an art into a technical discipline. In this blog, we’ll learn about the essential skills every prompt engineer needs, how they translate into practical use cases, and where to start if you’re new to the field.

Learn how to use typedef struct in C to simplify complex data type declarations and improve code readability.

Dynamic programming (DP) is one of the most intimidating topics in coding interviews. It’s abstract, often unintuitive, and the problems on LeetCode can seem overwhelming, especially when you’re just starting out. But here’s the good news: with the right mental models and consistent practice, anyone can get good at dynamic programming. In this blog, we’ll explain how to approach LeetCode dynamic programming problems, what patterns to focus on, and how to build the confidence to tackle even the toughest interview questions.

This blog emphasizes the role of Continuous Integration (CI) and Continuous Deployment (CD) in creating resilient software systems. In today's fast-paced digital environment, CI enables developers to frequently integrate code changes into a shared repository, with automated tests ensuring stability. CD automates the deployment of these changes, allowing for quick releases and minimizing downtime. Resilient systems are characterized by traits such as redundancy, fault tolerance, self-healing, scalability, and adaptability, which help maintain essential functions during disruptions. Companies like Netflix and AWS illustrate how CI/CD practices enhance system resilience. The CI/CD pipeline consists of Continuous Integration, Continuous Deployment, and Continuous Testing, promoting early bug detection and rapid recovery from failures. Integrating CI/CD with resilient system design is essential for building robust software that effectively responds to challenges and user needs.

Learn how to evaluate a RAG system using the Ragas library.