BRICS AI Economics

BRICS AI Economics

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Oct, 25 2026

Chinchilla Scaling Laws: The Compute-Optimal Ratio for LLM Training

Emily Fies
0
Discover how Chinchilla scaling laws optimize LLM training by balancing model size and data volume. Learn the 20:1 ratio, its limits, and practical application.
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Oct, 24 2026

Risk Management for Large Language Models: Controls and Escalation Paths

Emily Fies
0
Discover how to manage LLM risks effectively. Learn about dynamic controls, escalation paths, and governance strategies for generative AI.
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Oct, 23 2026

Privacy by Design Prompts: How to Instruct AI to Limit Data Collection

Emily Fies
1
Learn how to apply Privacy by Design principles to AI by crafting prompts that limit data collection. Discover strategies for data minimization, dynamic consent, and regulatory compliance in LLMs.
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Oct, 22 2026

Layered Architecture in Vibe-Coded Apps: Enforcing Separation of Concerns

Emily Fies
0
Discover how vibe coding impacts layered architecture and learn strategies to enforce separation of concerns in AI-generated codebases.
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Oct, 21 2026

The Hidden Price Tag: Change Management Costs in Generative AI Programs

Emily Fies
4
Discover the true cost of generative AI adoption. Learn why change management, training, and process redesign consume 15-25% of budgets and how strategic spending boosts ROI.
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Oct, 20 2026

Output Filtering: Blocking Sensitive and Harmful LLM Responses

Emily Fies
0
Discover how output filtering protects AI applications by blocking harmful or sensitive LLM responses. Learn about layered defense strategies, PII detection, and tools like AWS Bedrock and Cloudflare.
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Oct, 19 2026

Generative AI ROI: Real Case Studies and Lessons from Early Adopters

Emily Fies
0
Discover the real ROI of generative AI through case studies from Coca-Cola, Klarna, and healthcare. Learn why 95% of pilots fail and how successful adopters measure value beyond cost savings.
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Oct, 18 2026

Cutting LLM Costs: Prompt Length, Batching, and Caching Strategies

Emily Fies
5
Discover how to cut LLM costs by up to 80% using prompt trimming, batch processing, and semantic caching. Learn practical strategies to optimize AI spending without sacrificing quality.
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Oct, 17 2026

Stopping AI Hallucinations: Practical Mitigation Strategies for Generative Models

Emily Fies
0
Generative AI hallucinations occur when models produce plausible but false information. Learn practical mitigation strategies like RAG, RLHF, and prompt engineering to improve accuracy.
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Oct, 16 2026

Pair Reviewing with AI: Human + Model Code Review Workflows

Emily Fies
7
Stop waiting on slow code reviews. Learn how to integrate AI tools like GitHub Copilot and Greptile into your workflow to catch bugs faster while keeping humans focused on architecture.
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Oct, 15 2026

Auditing AI Usage: Essential Logs, Prompts, and Output Tracking

Emily Fies
8
Discover essential AI auditing practices for 2026. Learn how to log prompts, track outputs, and manage compliance risks without sacrificing performance.
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Oct, 14 2026

Scaling Laws in Practice: When to Stop Training Large Language Models

Emily Fies
0
Learn how to determine the optimal stopping point for LLM training. We compare Kaplan and Chinchilla scaling laws, explain why overtraining is common in production, and provide practical metrics for saving compute.

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