BRICS AI Economics

Category: AI Engineering - Page 2

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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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Jul, 27 2026

How to Prompt for Performance Profiling and Optimization Plans: A Developer’s Guide

Emily Fies
6
Learn how to craft precise prompts for AI to analyze performance profiling data and generate effective optimization plans. Includes templates, pitfalls, and real-world examples.
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Jul, 25 2026

Tokens per Parameter: The Real Data Ratio for Training LLMs

Emily Fies
9
Discover the optimal tokens per parameter ratio for training LLMs. Learn how scaling laws, the Chinchilla study, and data quality impact model efficiency and performance in 2026.
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Jul, 23 2026

Hybrid Search for RAG: Combining Semantic and Keyword Retrieval for LLMs

Emily Fies
0
Discover how hybrid search combines semantic and keyword retrieval to boost RAG accuracy. Learn about BM25, vector fusion, and implementation strategies for LLMs.
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Jul, 22 2026

How to Establish Coding Standards for Vibe-Coded Repositories in 2026

Emily Fies
0
Learn how to establish robust coding standards for vibe-coded repositories. Discover strategies for prompt engineering, context management with MCP, and automated safety layers to ensure maintainable AI-generated code.
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Jul, 19 2026

Parameter-Efficient Fine-Tuning: Mastering LoRA and Adapters for LLMs in 2026

Emily Fies
5
Learn how to fine-tune large language models efficiently using LoRA and Adapters. Discover the technical differences, implementation steps with Hugging Face PEFT, and 2026 trends like QLoRA and FlexLLM.
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Jul, 18 2026

Causal vs Bidirectional Attention: Tradeoffs in Modern LLMs

Emily Fies
7
Explore the critical tradeoffs between causal and bidirectional attention in modern LLMs. Learn how these mechanisms impact performance, speed, and suitability for different AI tasks.
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Jul, 16 2026

Data Augmentation for LLM Fine-Tuning: Synthetic and Human-in-the-Loop Approaches

Emily Fies
9
Learn how to boost LLM fine-tuning performance using synthetic data generation and human-in-the-loop strategies. Explore practical steps for data augmentation with LoRA and PEFT.

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