Maai & AI

Tagged: ai-tools

10 entries

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The best way to build a skill.md: start with AI, finish with humans | GitBook Blog

The article argues that the most effective workflow for creating skill.md files (structured instruction files for AI agents) combines AI-generated drafts with human refinement. It contrasts purely automated generation, which lacks nuance and product-specific judgment, with fully manual creation, which is time-consuming. The recommended hybrid approach leverages AI for structure and speed while relying on human expertise to add the contextual knowledge that makes AI agents actually effective.
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Just a moment...

This article examines the friction that arises when users shift from exploratory conversation with AI tools to precise task delegation. Through direct observation of seven people working with AI, the author identifies three interaction styles (collaborative, commanding, over-explainers) and reveals how conversational fluency in AI interfaces creates false expectations of shared understanding, leading users to accept suggestions that drift from their original intent.
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Does your new workflow look like any of these? Are you satisfied with your outcome from this workflows? Do you spend countless hours going back and forth with your AI tool to get the outcomes you… | Ovetta Sampson

This piece examines the iterative workflow patterns users develop when interacting with AI tools to achieve desired outcomes. It questions whether current AI-assisted workflows are effective or if they devolve into endless cycles of back-and-forth refinement, and points to Figma's exploration of AI-enabled product development as a case study in optimizing idea-to-product processes.
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Agentic AI design patterns: 2026 edition

This article presents a comprehensive architectural framework for agentic AI systems in 2026, arguing that most AI failures stem from architectural problems rather than model quality. The author defines four canonical design patterns (Reflection, Tool Use, Planning, and Multi-Agent) and emphasizes that agentic AI represents a paradigm shift from monolithic systems to distributed, observable, and bounded agent architectures.
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State of RAG & GenAI

This article argues that Retrieval Augmented Generation (RAG) has become a strategic imperative for enterprises in 2026, addressing critical challenges like LLM hallucinations, outdated outputs, and high retraining costs. RAG bridges the gap between large language models and organizational knowledge by retrieving verified, real-time data at the moment of generation, ensuring outputs are accurate, compliant, and trustworthy without requiring constant model retraining.
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AI metaphors: how to think about AI

Argues that the mental model you bring to AI determines how effectively you work with it. Catalogues thirteen metaphors (bionic mind, smart intern, word calculator, genie, sparring partner, and others), each surfacing different affordances and risks of LLMs. The framing is operational: pick the metaphor that fits the task, switch deliberately, avoid being trapped by one.