BRICS AI Economics

BRICS AI Economics - Page 3

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Jan, 31 2026

Self-Consistency Prompting in Generative AI: How Voting Strategies Boost Accuracy

Emily Fies
8
Self-consistency prompting boosts AI accuracy by generating multiple reasoning paths and selecting the most common answer. It works best on math, logic, and medical tasks - not creative writing. Learn how to use it effectively.
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Jan, 30 2026

Safety Policies for Legal Use of Generative AI: Lessons from Mata v. Avianca

Emily Fies
8
The Mata v. Avianca case exposed the dangers of using generative AI for legal citations. Learn how lawyers got sanctioned for fabricating cases with ChatGPT-and what safety policies now prevent this from happening again.
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Jan, 29 2026

Edge-Capable Multimodal Large Language Models: What They Can Do and Where They Fall Short

Emily Fies
10
Edge-capable multimodal LLMs like MiniCPM-V run AI on phones without the cloud, offering privacy and speed-but they still have limits in battery life, accuracy, and complexity. Here's what they can do now and where they fall short.
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Jan, 28 2026

Event-Driven Architectures with Vibe Coding: Patterns and Prompt Templates

Emily Fies
10
Learn how to use vibe coding with event-driven architecture to build scalable systems faster. Discover proven patterns, effective prompt templates, and how frameworks like Ecotone reduce AI errors and technical debt.
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Jan, 27 2026

Executive Education on Generative AI: What Boards and C-Suite Leaders Need to Know in 2026

Emily Fies
9
Executive education programs in generative AI are now essential for boards and C-suite leaders. Learn which programs deliver real strategy, not just theory, and how to choose one that drives actual business change in 2026.
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Jan, 26 2026

Infrastructure Requirements for Serving Large Language Models in Production

Emily Fies
7
Serving large language models in production requires specialized hardware, smart scaling, and cost-aware architecture. Learn the real GPU, storage, and network needs-and how to avoid common pitfalls.
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Jan, 23 2026

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Emily Fies
6
Transformers revolutionized AI by letting language models understand context instantly. Learn how self-attention, positional encoding, and multi-head attention power today’s top LLMs - and why they’re replacing older models.
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Jan, 23 2026

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Emily Fies
9
Transformers revolutionized AI by enabling large language models to understand context across long texts using self-attention. This article explains how they work, why they beat older models, and what’s changing in 2025.
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Jan, 22 2026

How Large Language Models Are Transforming Healthcare Documentation and Triage

Emily Fies
5
Large language models are cutting documentation time for doctors and improving triage accuracy in emergency rooms. But bias, integration costs, and regulatory gaps remain major hurdles. Here’s how they’re really being used in U.S. healthcare today.
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Jan, 21 2026

Centralized Prompt Libraries: How Teams Build Reusable AI Patterns and Standards

Emily Fies
10
Centralized prompt libraries turn chaotic AI use into repeatable, consistent workflows. Learn how teams use curated prompts to save time, reduce errors, and scale AI across departments.
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Jan, 20 2026

How to Measure ROI for Large Language Model Projects: Real Metrics That Drive Decisions

Emily Fies
9
Learn the real metrics that prove LLM ROI-time saved, user adoption, hallucination rates, and cost comparisons. Stop guessing. Start measuring what matters.
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Jan, 19 2026

Sustainability of AI Coding: How Energy, Cost, and Efficiency Trade-Offs Are Reshaping Development

Emily Fies
10
AI coding is accelerating climate impact. Learn how energy use, carbon emissions, and efficiency trade-offs are reshaping development-and what you can do about it in 2026.

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