BRICS AI Economics

Category: Business

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Apr, 4 2026

Employment Law and Generative AI: A Guide to Worker Rights and Compliance in 2026

Emily Fies
0
Explore the intersection of employment law and Generative AI in 2026. Learn about worker rights, state-level regulations in CA, CO, TX, and NY, and how to avoid algorithmic discrimination.
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Mar, 28 2026

How to Budget for Vibe Coding Platforms: Licenses, Models, and Cloud Costs Explained

Emily Fies
8
Navigate unpredictable vibe coding platform costs with clear strategies for licenses, AI model pricing, and cloud expenses. Learn to budget effectively in 2026.
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Mar, 25 2026

Diverse Teams in Generative AI Development: Reducing Bias through Inclusion

Emily Fies
0
Explore how diverse teams in Generative AI Development reduce algorithmic bias. Learn practical steps, regulatory requirements, and the business case for inclusion in AI ethics.
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Mar, 24 2026

Prompting as Programming: How Natural Language Became the Interface for LLMs

Emily Fies
7
Prompting has replaced coding for many tasks, turning natural language into the new programming interface for LLMs. Learn how system prompts, Chain of Thought, and generated knowledge are reshaping how we interact with AI.
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Mar, 23 2026

Grounding Prompts in Generative AI: How Retrieval-Augmented Generation Cites Sources to Stop Hallucinations

Emily Fies
0
Grounding prompts with Retrieval-Augmented Generation stops AI hallucinations by forcing responses to cite real data. Learn how RAG works, where it excels, and why it's the only reliable way to use AI in business.
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Mar, 22 2026

Autoscaling Large Language Model Services: Policies, Signals, and Costs

Emily Fies
7
Autoscaling LLM services requires specialized metrics like prefill queue size and slots_used - not CPU or GPU usage. Learn how to reduce costs by 30-60% while keeping latency low, and avoid the pitfalls that waste millions in cloud spend.
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Mar, 20 2026

How Prompt Templates Reduce Waste in Large Language Model Usage

Emily Fies
0
Prompt templates cut LLM waste by 65-85% by reducing token use, energy, and processing time. Learn how structured prompts save money, lower emissions, and improve output - without changing your model.
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Mar, 19 2026

Cost Savings from Compression: How LLM Efficiency Drives Real Business Value

Emily Fies
6
LLM compression cuts infrastructure costs by up to 80% through quantization, pruning, distillation, and prompt compression. Real companies are saving millions - here’s how to build your business case.
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Mar, 18 2026

Versioning Contracts in Vibe-Coded APIs: Preventing Breaking Changes

Emily Fies
9
Learn how versioning contracts in Vibe-coded APIs prevent breaking changes using semantic versioning, automated OpenAPI specs, and a strict deprecation policy. A practical guide for teams building reliable APIs with AI-assisted development.
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Mar, 16 2026

Latency Budgets for Interactive Large Language Model Applications

Emily Fies
10
Latency budgets determine whether your AI app feels responsive or frustrating. Learn how TTFT, batching, model size, and caching shape real-world performance for interactive LLM applications.
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Mar, 15 2026

Rotary Position Embeddings and ALiBi: How Modern LLMs Handle Sequence Order

Emily Fies
9
Rotary Position Embeddings and ALiBi are two modern methods that help large language models understand word order without traditional positional encodings. Both improve long-context handling, scalability, and efficiency.
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Mar, 14 2026

vLLM vs TGI: Which LLM Serving Framework Delivers More Power for Your API?

Emily Fies
10
vLLM and TGI are two leading frameworks for serving large language models. vLLM delivers higher throughput and memory efficiency, while TGI offers easier deployment and better observability. Choose based on your traffic, model size, and team workflow.

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