BRICS AI Economics

Mar, 27 2026

Robustness and Generalization Tests for Large Language Model Reliability

Emily Fies

Learn essential robustness testing methods for LLMs beyond standard benchmarks, including adversarial stress tests, OOD validation, and real-world deployment readiness.

Mar, 25 2026

Diverse Teams in Generative AI Development: Reducing Bias through Inclusion

Emily Fies

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.

Mar, 24 2026

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

Emily Fies

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.

Mar, 23 2026

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

Emily Fies

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.

Mar, 22 2026

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

Emily Fies

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.

Mar, 20 2026

How Prompt Templates Reduce Waste in Large Language Model Usage

Emily Fies

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.

Mar, 19 2026

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

Emily Fies

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.

Mar, 18 2026

Versioning Contracts in Vibe-Coded APIs: Preventing Breaking Changes

Emily Fies

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.

Mar, 17 2026

Security Basics for Non-Technical Builders Using Vibe Coding Platforms

Emily Fies

Non-technical builders using AI coding tools like Replit or GitHub Copilot need to understand basic security - or risk exposing secrets, data, and money. Learn the 4 must-do steps to protect your vibe-coded apps.

Mar, 16 2026

Latency Budgets for Interactive Large Language Model Applications

Emily Fies

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.

Mar, 15 2026

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

Emily Fies

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.

Mar, 14 2026

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

Emily Fies

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.

BRICS AI Economics

Robustness and Generalization Tests for Large Language Model Reliability

Diverse Teams in Generative AI Development: Reducing Bias through Inclusion

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

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

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

How Prompt Templates Reduce Waste in Large Language Model Usage

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

Versioning Contracts in Vibe-Coded APIs: Preventing Breaking Changes

Security Basics for Non-Technical Builders Using Vibe Coding Platforms

Latency Budgets for Interactive Large Language Model Applications

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

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

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Latest Courses

Diverse Teams in Generative AI Development: Reducing Bias through Inclusion

NLP Pipelines vs End-to-End LLMs: When to Use Traditional Processing vs Prompting

How Synthetic Data Generation Protects Privacy in LLM Training

Parameter-Efficient Generative AI: LoRA, Adapters, and Prompt Tuning at Scale

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

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