Best of Prompt Engineering — April 2025

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   Article

   Neon·1y

   Prompt Engineering as a Developer Discipline

   Structured prompting is becoming a crucial skill for developers, akin to traditional coding practices. Using AI effectively involves treating prompts as modular, testable components within software systems. Techniques like few-shot prompting, chain-of-thought reasoning, self-consistency, skeleton prompting, and configuration parameters improve AI's coding outputs. Developers should rigorously validate and maintain prompts, just like any other code, to ensure reliability and consistency in AI-powered features.

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   Article

   portkey·1y

   Portkey Prompt Engineering Studio User-Centered Design Case Study

   Portkey's Prompt Engineering Studio was redesigned to enhance usability and scalability for enterprise teams. The redesign focused on addressing user pain points such as inefficient prompt testing, cluttered interfaces, and difficult model selection. Key improvements include a dynamic comparison view, simplified AI model selection, and streamlined tool integrations. The new design has led to faster testing, increased prompt testing per session, and improved overall productivity.

   57
3. 3
   Article

   portkey·1y

   Prompting Claude 3.5 vs 3.7

   Claude 3.7 Sonnet offers significant improvements over Claude 3.5 Sonnet in accuracy, reasoning, creativity, and various industry-specific applications. It provides more structured and detailed solutions, better handling of edge cases in coding, and enhanced creativity in writing prompts. Claude 3.7 Sonnet's refinements ensure better performance and user experience for businesses, developers, and content creators.

   52

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   Article

   Lightbend·1y

   Demystifying AI, LLMs, and RAG

   Kevin Hoffman simplifies the topics of vectors, embeddings, prompts, prompt engineering, RAG, agentic, and agentic AI in a developer-friendly manner.

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5. 5
   Article

   portkey·1y

   The hidden technical debt in LLM apps

   Interest in large language models (LLMs) has surged, leading to hidden technical debt that can harm scalability, maintainability, and cost-efficiency. Key areas of concern in LLM apps include prompt engineering, fragile pipelines, lack of observability and feedback, and cost unpredictability. Effective management strategies include investing in prompt management systems, implementing observability, automating evaluation and feedback loops, abstracting model providers, centralizing cost controls, and enforcing security and compliance. Using LLMOps tools can help mitigate these issues and build sustainable AI products.

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