Tag: LLM reasoning
Fine-Tuning for Faithfulness in Generative AI: Supervised and Preference Approaches
Explore how supervised and preference-based fine-tuning impacts LLM faithfulness. Learn to reduce hallucination risk using SFT, RLHF, and QLoRA techniques with real-world metrics.
- May 24, 2026
- Collin Pace
- 0
- Permalink
Chain-of-Thought in Vibe Coding: Why Explanations Must Come Before Code
Discover how Chain-of-Thought prompting transforms vibe coding by requiring LLMs to explain logic before writing code, reducing errors and speeding up debugging.
- Apr 15, 2026
- Collin Pace
- 7
- Permalink
Why Smarter AI Reasoning Might Actually Be More Dangerous
Explore why increased reasoning in LLMs creates new safety vulnerabilities, from context window degradation to the risks of distilled models.
- Apr 12, 2026
- Collin Pace
- 6
- Permalink
Chain-of-Thought Prompting in Generative AI: Master Step-by-Step Reasoning for Complex Tasks
Chain-of-thought prompting improves AI reasoning by making models show their work step by step. Learn how it boosts accuracy on math, logic, and decision tasks-and when it's worth the cost.
- Jan 29, 2026
- Collin Pace
- 7
- Permalink