Tag: few-shot prompting

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.

Causal Masking in Decoder-Only LLMs: How It Prevents Information Leakage and Powers Generative AI

Dec, 28 2025
Refusal-Proofing Security Requirements: Prompts That Demand Safe Defaults

Dec, 16 2025
Positional Encoding in Transformers: Sinusoidal vs Learned for Large Language Models

Dec, 14 2025
Vision-Language Applications with Multimodal Large Language Models: What’s Working in 2025

Dec, 26 2025
In-Context Learning Explained: How LLMs Learn from Prompts Without Training

Feb, 6 2026

Tag: few-shot prompting

Few-Shot Prompting Strategies That Boost LLM Accuracy and Consistency

Recent Post

Causal Masking in Decoder-Only LLMs: How It Prevents Information Leakage and Powers Generative AI

Refusal-Proofing Security Requirements: Prompts That Demand Safe Defaults

Positional Encoding in Transformers: Sinusoidal vs Learned for Large Language Models

Vision-Language Applications with Multimodal Large Language Models: What’s Working in 2025

In-Context Learning Explained: How LLMs Learn from Prompts Without Training

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