BRICS AI Economics

Category: Business - Page 3

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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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Jan, 18 2026

Human Feedback in the Loop: How to Score and Refine AI Code Iterations for Better Results

Emily Fies
8
Human Feedback in the Loop turns AI coding from guesswork into a learning system. Learn how scoring AI suggestions cuts bugs, improves code quality, and helps teams build better software faster.
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Jan, 17 2026

Domain Adaptation in NLP: How to Fine-Tune Large Language Models for Medical, Legal, and Financial Text

Emily Fies
9
Learn how to fine-tune large language models for medical, legal, and financial text using domain adaptation. Discover methods, costs, pitfalls, and real-world results.
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Jan, 15 2026

Multimodal Agents in Generative AI: Tools That See, Hear, and Act

Emily Fies
7
Multimodal AI agents see, hear, and act like humans - processing images, sound, and text together to understand context and respond intelligently. Learn how they're transforming healthcare, manufacturing, and customer service - and where they still fall short.
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Jan, 14 2026

Refusal-Proofing Security Requirements: Prompts That Demand Safe Defaults

Emily Fies
10
Refusal-proof security requirements eliminate guesswork by making safe defaults mandatory. Learn how to write enforceable security rules that block insecure code before it ships, using OWASP ASVS, SQUARE, and STRIDE frameworks.
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Jan, 10 2026

Forecasting Delivery Timelines with Vibe Coding Data: How AI Is Changing Software Deadlines

Emily Fies
6
Vibe Coding is transforming software delivery by using AI to generate code from natural language prompts. Teams now forecast timelines based on AI speed, not human velocity - cutting development from weeks to days. Learn how it works, who benefits, and where it still falls short.
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Jan, 7 2026

Structured vs Unstructured Pruning for Efficient Large Language Models

Emily Fies
5
Structured and unstructured pruning help shrink large language models for faster, cheaper deployment. Structured pruning works on any device; unstructured offers higher compression but needs special hardware. Here's how to choose the right one.
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Jan, 4 2026

Data Residency Considerations for Global LLM Deployments

Emily Fies
5
Global LLM deployments must comply with data residency laws like GDPR and PIPL. Learn how hybrid architectures, SLMs, and regional infrastructure help avoid fines and keep user data local.
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Jan, 1 2026

Prompt Sensitivity Analysis: How Small Changes in Instructions Break LLM Performance

Emily Fies
0
Small changes in how you phrase a prompt can cause massive swings in LLM performance. Learn why prompt sensitivity breaks AI systems, which models are most vulnerable, and how to test and fix it before it costs you money.
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Dec, 30 2025

How to Detect Implicit vs Explicit Bias in Large Language Models

Emily Fies
7
Large language models may seem fair on the surface, but hidden biases persist-even in the most advanced systems. Learn how to detect implicit bias that standard tests miss and why bigger models aren't necessarily fairer.
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Dec, 29 2025

Human-in-the-Loop Operations for Generative AI: Review, Approval, and Exceptions

Emily Fies
6
Human-in-the-loop operations for generative AI ensure AI outputs are reviewed, approved, and corrected by humans before deployment - critical for compliance, safety, and trust in regulated industries.

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