BRICS AI Economics

Author: Emily Fies

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Aug, 9 2026

How Autoregressive Generation Works: Step-by-Step Token Production in LLMs

Emily Fies
0
Explore how autoregressive generation works in large language models. Learn the step-by-step process of token production, causal masking, and the limitations of sequential AI text generation.
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Aug, 8 2026

Knowledge Management with LLMs: Building Enterprise Q&A Over Internal Documents

Emily Fies
0
Learn how to build secure, accurate enterprise Q&A systems using LLMs and RAG architecture. Covers vector databases, hallucination mitigation, and implementation costs.
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Aug, 7 2026

How Think-Tokens Change Generation: Reasoning Traces in Modern Large Language Models

Emily Fies
1
Explore how think-tokens and reasoning traces transform LLM generation, boosting accuracy by 37% while adding latency. Learn the mechanics, efficiency trade-offs, and optimization strategies for modern AI models.
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Aug, 6 2026

Enterprise Vibe Coding Certification: Pathways, Costs, and Governance in 2026

Emily Fies
0
Explore top vibe coding certifications for enterprises in 2026. Compare costs, governance features, and security benefits of programs from ServiceNow, Stanford, and ADaSci.
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Aug, 5 2026

Prompt-to-Response Latency in LLMs: The Real Mechanics Behind the Delay

Emily Fies
6
Discover the real mechanics behind LLM latency. We break down Time to First Token (TTFT) and Inter-Token Latency (ITL), explaining how KV caches, sequential generation, and hardware choices impact your app's speed.
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Aug, 3 2026

Next-Gen AI Hardware 2026: Accelerators, HBM4 Memory, and Networking Trends

Emily Fies
0
Explore the 2026 AI hardware landscape: NVIDIA Rubin, AMD Helios, and Microsoft Maia 200. Learn how HBM4 memory and new networking architectures shape generative AI performance.
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Aug, 2 2026

Trunk-Based Development vs GitFlow for AI Teams: Which Strategy Wins?

Emily Fies
0
Compare Trunk-Based Development and GitFlow for AI engineering teams. Learn which branching strategy improves maintainability, reduces merge conflicts, and accelerates model experimentation.
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Aug, 1 2026

Prompt Chaining in Generative AI: Breaking Complex Tasks into Reliable Steps

Emily Fies
0
Learn how prompt chaining breaks complex AI tasks into reliable steps, reducing hallucinations by 67%. Explore techniques, pitfalls, and real-world examples for better LLM performance.
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Jul, 31 2026

Test Coverage Targets for AI-Generated Code: Realistic Metrics and Best Practices

Emily Fies
0
Discover realistic test coverage targets for AI-generated code. Learn why 80% is often insufficient and how risk-based strategies, mutation testing, and path coverage improve software quality.
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Jul, 30 2026

How Large Language Models Use Probabilities to Choose Words and Phrases

Emily Fies
0
Explore how Large Language Models use conditional probability, softmax functions, and decoding strategies like top-p and temperature to choose words. Learn why this statistical approach leads to both impressive creativity and occasional hallucinations.
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Jul, 29 2026

Task Decomposition Strategies for Planning in Large Language Model Agents

Emily Fies
0
Explore task decomposition strategies for LLM agents, including ACONIC, Chain-of-Thought, and Chain-of-Code. Learn how breaking down complex tasks improves accuracy by up to 40% and reduces costs.
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Jul, 28 2026

Prompt Libraries for Teams: How to Standardize AI Requests for Consistent Output

Emily Fies
0
Learn how to build a prompt library for your team to standardize AI requests, improve output consistency, and boost productivity with proven strategies and tools.

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