Managing costs when working with large language models (LLMs) can be tricky. This guide shows how to create a smart routing system that directs prompts to the most suitable model based on complexity. Using local prompt classification and model switching, developers can save money while maintaining performance. Setting Up the Environment and Tools The first
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All posts tagged in Prompt Engineering
Claude Code is transforming how developers approach AI-powered programming. From building simple apps to creating complex workflows, this tool offers endless possibilities. If you’re curious about how to unlock its potential, here are five fun and practical projects to get started with. Start by Building a Basic Web App This project is perfect for beginners.
Many developers see prompting as just a way to get quick answers from AI models. They often write a prompt, see the output, and tweak it if needed. But as AI systems move into real-world use, consistency and reliability become crucial. Just getting an answer once isn’t enough; the prompts need to produce dependable results
Security experts warn that public web pages are increasingly being used to secretly manipulate artificial intelligence agents. These attacks involve embedding invisible commands within normal website content that AI systems unknowingly process. As AI becomes more integrated into business workflows, this kind of manipulation poses a serious threat. The Rise of Indirect Prompt Injections Traditional
Many companies have been rushing to prepare their teams for AI integration over the past couple of years. They initially focused on training employees to craft prompts and operate chatbots. But as AI becomes a part of everyday workflows, it turns out that those skills aren’t the most important ones. Instead, the real skills for
Back in the 1990s and early 2000s, many developers appreciated the certainty that came with traditional compilers. You wrote your code, the compiler analyzed and optimized it, and then produced machine instructions that matched your expectations exactly. Every time you ran the code, you got the same output. This consistency shaped how a whole generation
ChatGPT has become a staple in daily life, with over 2.5 billion prompts processed each day in 2026. People rely on it for writing, research, problem-solving, and more. As it becomes part of the infrastructure like search engines once were, many users still find themselves frustrated with inconsistent or superficial answers. The key to better
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A recent study from Microsoft reveals that a single simple prompt can significantly weaken safety measures in leading AI and image models. This discovery raises concerns about how reliable these models are when customized for business use. The research shows that even harmless-sounding prompts can make these models more permissive and less safe across many
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In the world of artificial intelligence, especially with large language models, understanding how the system processes information is key. This is where context engineering comes into play. It’s a new way to design AI systems by carefully shaping what information the model sees before it generates a response. Instead of just crafting prompts, this approach
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AI-assisted coding tools are transforming how applications are built. They enable faster development and make prototyping accessible to more people, including those without traditional coding skills. However, rushing into production without proper checks can lead to serious security and stability issues. It’s essential to understand how these tools work and the risks involved before deploying
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