Best of Prompt Engineering — December 2025

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    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·20w

    The AI Engineering Guidebook

    A comprehensive 350+ page guidebook covering the engineering fundamentals of LLM systems, including model architecture, training, prompt engineering, RAG systems, fine-tuning techniques like LoRA, AI agents, Model Context Protocol, optimization strategies, and deployment considerations. The resource focuses on practical engineering decisions, system design tradeoffs, and real-world implementation patterns rather than surface-level usage.

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    Article
    Avatar of weaviateWeaviate·22w

    Context Engineering for AI Agents

    Context engineering is the discipline of designing systems that feed LLMs the right information at the right time, addressing the fundamental constraint of finite context windows. It encompasses six interdependent pillars: agents that orchestrate decisions, query augmentation that refines user input, retrieval that connects to external knowledge, prompting that guides reasoning, memory that preserves history, and tools that enable real-world action. Unlike prompt engineering which focuses on phrasing instructions, context engineering builds the architecture around the model, treating the context window as a scarce resource and designing retrieval, memory systems, and tool integrations to maximize signal while avoiding context poisoning, distraction, confusion, and clash.

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    Article
    Avatar of hnHacker News·22w

    I failed to recreate the 1996 Space Jam Website with Claude

    An engineer attempts to use Claude AI to recreate the iconic 1996 Space Jam website from screenshots and assets, but fails despite multiple approaches. The experiment reveals Claude's limitations in spatial reasoning and precise visual measurements. Despite providing grids, comparison tools, and zoomed images, Claude consistently produces inaccurate layouts while confidently claiming success. The author theorizes this stems from how vision models process images in 16x16 patches, losing fine-grained spatial detail. The piece documents the iterative debugging process, Claude's unreliable self-assessment, and the surprising difficulty of a seemingly simple HTML recreation task.

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    Video
    Avatar of youtubeYouTube·21w

    4-Step Gemini 3.0 Pro System For Beautiful UI Designs

    A four-step workflow for generating UI designs using Gemini 3.0 Pro before building functionality. The process starts with creating a product requirements document (PRD), extracting core features and UX considerations, building a design system based on visual inspiration, and finally generating screen-by-screen designs with all states. The approach emphasizes designing upfront rather than treating UI as an afterthought, using structured prompts to guide the AI through creating a complete design system with colors, typography, and component specifications that can be implemented in React with Tailwind CSS.

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    Article
    Avatar of programmingdigestProgramming Digest·22w

    AI Skeptic to AI Pragmatist

    A developer shares their journey from AI skepticism to pragmatic adoption after months of hands-on experience with LLMs and AI coding assistants. Key learnings include the importance of providing structured context (who, what, why, how), treating AI as a collaborator rather than magic, using git commits as save points, switching between models for different tasks, and creating MCP servers for framework-specific knowledge. The author emphasizes that AI requires a learning curve and intentional practice to use effectively, advocating for industry discussions that acknowledge AI's utility while addressing ethical concerns.

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