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All digests tagged Prompt Engineering
To make AI apps look professional, developers must focus on UX polish and avoid common 'slop' patterns (like purple gradients). Use visual references, break tasks into small prompts, and iterate with smaller open-source models.
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Applied vertical AI's true moat is not the model or infrastructure, but proprietary data and domain expertise. The key to success is narrowing the problem scope and integrating human experts into a continuous learning loop.
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Shipping AI safely requires moving beyond A/B testing. The solution involves building an evaluation stack using simulation (Matrix) and automated prompt optimization (Jeppa) to prove safety before patient exposure.
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Mastering the System Prompt is key to controlling AI agents. Learn how to use positive/negative patterns, reference points (D1/R6), and hard operational boundaries to turn verbose LLMs into precise senior engineers.
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Martin Keen explores how Rapid Application Development (RAD) principles map onto AI coding. Learn why formal specifications are crucial for moving from fast prototypes to secure, production-ready software.
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Unify achieved a 90-95% cost optimization by treating subagents as simple function calls. Key strategies include optimizing prompt cache hits, using specialized REPLs like Monty for durable cloud execution, and implementing Pandas-like wrappers over databases.
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AI News & Strategy Daily | Nate B Jones
To manage long-running AI agents, focus on 'Progressive Context Shaping.' Instead of relying on massive context windows, externalize and update the current project state in structured files (e.g., `current.markdown`) to guide the agent's direction.
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Context Engineering is the critical practice of structuring information passed to LLMs and AI agents. Learn how optimizing context, rather than just writing better prompts, improves reliability and reduces hallucinations in complex AI systems.
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Learn how prompt caching can dramatically cut the input costs of long AI agent sessions. Discover why standard LLM caching misconceptions are wrong, and learn best practices for building cache-friendly agent harnesses.
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The key to advanced agentic engineering is realizing that agents plus code beats agents alone. The Super Simple Software Factory automates the full SDLC using deterministic checks and three principles: Observable, Customizable, and Reusable.
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AI News & Strategy Daily | Nate B Jones
AI agents use skills, which are sets of instructions that must be designed to be readable by humans and useful to AI. Learn how to audit your skill library and avoid performance conflicts.
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Ali Khial critiques current AI coding benchmarks, detailing how vague instructions and weak verifiers allow models to 'game' tests. He outlines five principles for creating trustworthy, production-grade evaluation systems.
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This session guides developers from LLM theory to practical agentic implementations, detailing how primitives like structured output, tool calling, and guardrails build robust AI systems.
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AI News & Strategy Daily | Nate B Jones
Running out of AI tokens? Learn how 'reused input' dominates token usage in LLMs. We cover 15 rules to optimize your workflow, from editing mistakes to using local frameworks.
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This technical talk explores LLM vulnerabilities, detailing attacks like Policy Puppetry and computational graph backdoors. Learn how to secure AI systems against sophisticated threats.
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DSPy brings software engineering principles to AI programs by defining tasks via a 'Signature' (inputs/outputs). This separates the task contract from the model, allowing flexible optimization and scaling.
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The winning mindset in AI is 'AND,' not 'OR.' Model Fusion combines compute from multiple state-of-the-art agents (e.g., GPT 5.6 Sol, Claude Fable 5) using custom commands like /opinion and /auto validate to achieve superior engineering results.
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Learn how to build functional web apps using AI by mastering prompt engineering. We break down the five building blocks: Goal, Input, Layout, Features, and Output.
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