Tag: chain-of-thought

Few-Shot Prompting Strategies That Boost LLM Accuracy and Consistency

Few-shot prompting boosts LLM accuracy by 15-40% using just 2-8 examples. Learn how to choose the right examples, avoid over-prompting, and combine it with chain-of-thought for better results - without fine-tuning.

Calibration and Confidence Metrics for Large Language Model Outputs: How to Tell When an AI Is Really Sure

Aug, 22 2025
Red Teaming for Privacy: How to Test Large Language Models for Data Leakage

Jan, 10 2026
Data Collection and Cleaning for Large Language Model Pretraining at Web Scale

Dec, 30 2025
Model Parallelism and Pipeline Parallelism in Large Generative AI Training

Feb, 3 2026
Prompt Hygiene for Factual Tasks: How to Write Clear LLM Instructions That Don’t Lie

Sep, 12 2025

Tag: chain-of-thought

Few-Shot Prompting Strategies That Boost LLM Accuracy and Consistency

Recent Post

Calibration and Confidence Metrics for Large Language Model Outputs: How to Tell When an AI Is Really Sure

Red Teaming for Privacy: How to Test Large Language Models for Data Leakage

Data Collection and Cleaning for Large Language Model Pretraining at Web Scale

Model Parallelism and Pipeline Parallelism in Large Generative AI Training

Prompt Hygiene for Factual Tasks: How to Write Clear LLM Instructions That Don’t Lie

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