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Insights, updates, and thought leadership on AI-native analytics.

Diagram of context engineering showing statistical context, business logic, and session memory feeding into a language model to determine the next step

AI

AI Analyst Prompt Engineering Beyond Coding: Context Engineering for Analytics Workflows

In this first article in our Prompting Context Engineering for Analytics series, we are sharing learnings from our experience building

Diagram showing how AI Analysts leverage LLMs to shift capabilities across the analytics stack, from data engineers to stakeholders, boosting ROI by 150%.

AI

The Rise of the AI Analyst

Just as the rise of foundation models created the “AI Engineer” role (1) transforming software engineers productivity (2), we’re witnessing

Context Engineering Stack diagram showing RIFFML as XML execution engine for multi-agent workflows, JAM as memory system for contextual relevance, and SWING as semantic security framework for AI systems, addressing orchestration challenges in production AI.

AI

The Context Engineering Stack

Context engineering has emerged as a critical discipline for production AI systems. While models grow more capable, the challenge isn’t

Comparison chart showing differences between coding and analytics problem solving. Coding uses structured syntax, linear workflows, and clear correctness criteria, while analytics relies on domain-specific language, iterative exploration, and context-dependent success.

AI

From Code to Context: Building AI That Thinks Like an Analyst, Not a Coder

The Architecture of Understanding: Core Systems Behind AI-Native Analytics In this Part 3 of our “What it takes to make

Diagram explaining when to use AI coding assistants, direct LLM usage, or manual coding. Highlights how AI handles routine coding while the profession splits between system thinkers and pure coders, widening the skill gap.

AI

When should you fire your AI Coding Assistant?

Coding Assistants in Action: WindSurf, Cursor and Roo’s Approach to Complex Feature Implementation …and how Generative AI changes Software Engineering

3D-rendered robot with a cylindrical head and antenna walking through a foggy, surreal mountain landscape at sunrise, with soft pastel lighting and floating clouds.

AI

When to Fire Your AI Assistant: The Hidden Architecture of Cursor and Windsurf

…and how it informs the new coding paradigm for Software Engineers This article is also published as Part of Ask-Y’s

Diagram comparing coding workflow and analytics workflow. Coding follows a linear sequence of design, implement, test, and deploy with clear milestones. Analytics workflow uses continuous iterative cycles of transform, analyze, output, and integrate to refine insights.

AI

What it takes to make AI Native Analytics work in the real world

When we think of Native AI Assistants today, the first thing that comes to mind are coding assistants like GitHub

Bar chart comparing development time for integrating speech-to-text: 40 hours without tools, 8–16 hours with prior experience, 6 hours using LLM directly, and 3 hours using a coding assistant.

AI

How We’re Building an AI-Native COMPANY (Not Just an AI-Native PRODUCT)

When we started Ask-Y to build our analytics platform, we made a conscious decision that would impact us more deeply

Diagram of context engineering showing statistical context, business logic, and session memory feeding into a language model to determine the next step

AI

AI Analyst Prompt Engineering Beyond Coding: Context Engineering for Analytics Workflows

In this first article in our Prompting Context Engineering for Analytics series, we are sharing learnings from our experience building

Diagram showing how AI Analysts leverage LLMs to shift capabilities across the analytics stack, from data engineers to stakeholders, boosting ROI by 150%.

AI

The Rise of the AI Analyst

Just as the rise of foundation models created the “AI Engineer” role (1) transforming software engineers productivity (2), we’re witnessing

Context Engineering Stack diagram showing RIFFML as XML execution engine for multi-agent workflows, JAM as memory system for contextual relevance, and SWING as semantic security framework for AI systems, addressing orchestration challenges in production AI.

AI

The Context Engineering Stack

Context engineering has emerged as a critical discipline for production AI systems. While models grow more capable, the challenge isn’t

Comparison chart showing differences between coding and analytics problem solving. Coding uses structured syntax, linear workflows, and clear correctness criteria, while analytics relies on domain-specific language, iterative exploration, and context-dependent success.

AI

From Code to Context: Building AI That Thinks Like an Analyst, Not a Coder

The Architecture of Understanding: Core Systems Behind AI-Native Analytics In this Part 3 of our “What it takes to make

Diagram explaining when to use AI coding assistants, direct LLM usage, or manual coding. Highlights how AI handles routine coding while the profession splits between system thinkers and pure coders, widening the skill gap.

AI

When should you fire your AI Coding Assistant?

Coding Assistants in Action: WindSurf, Cursor and Roo’s Approach to Complex Feature Implementation …and how Generative AI changes Software Engineering

3D-rendered robot with a cylindrical head and antenna walking through a foggy, surreal mountain landscape at sunrise, with soft pastel lighting and floating clouds.

AI

When to Fire Your AI Assistant: The Hidden Architecture of Cursor and Windsurf

…and how it informs the new coding paradigm for Software Engineers This article is also published as Part of Ask-Y’s

Diagram comparing coding workflow and analytics workflow. Coding follows a linear sequence of design, implement, test, and deploy with clear milestones. Analytics workflow uses continuous iterative cycles of transform, analyze, output, and integrate to refine insights.

AI

What it takes to make AI Native Analytics work in the real world

When we think of Native AI Assistants today, the first thing that comes to mind are coding assistants like GitHub

Bar chart comparing development time for integrating speech-to-text: 40 hours without tools, 8–16 hours with prior experience, 6 hours using LLM directly, and 3 hours using a coding assistant.

AI

How We’re Building an AI-Native COMPANY (Not Just an AI-Native PRODUCT)

When we started Ask-Y to build our analytics platform, we made a conscious decision that would impact us more deeply

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Attention Head Wars: Why Your AI Misclassifies

Why do LLMs still misclassify data even with many examples?Because attention heads compete. What does this episode explain?In the final

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Mode Collapse

Yelling at an LLM because it gave you the same unwanted answer for the third time?It’s not being stubborn. It’s

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Context Poisoning

Ever corrected an LLM five times and still gotten the same kind of answer?That isn’t stubbornness – it’s context poisoning.

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Attention Dilution

Want to know how to make an LLM remember precise details from a long conversation?Analytics workflows with LLMs automatically mean

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It’s All About Data

Getting your product into ChatGPT’s Storefront is all about… data. What happens when someone asks ChatGPT “find me Halloween costumes

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The Great Training

Ever wonder what’s actually happening inside an AI model when it learns?We did too… so we made the Floofies –