Pithy Cyborg | AI News Made Simple
Pithy Cyborg | AI News Made Simple
Prompting looks simple from the outside, but getting reliable results from AI systems is harder than most people expect. In this category, I write about prompt engineering, prompt templates, prompt failure modes, structured outputs, model-specific prompt behavior, and why prompts that work perfectly in one system can fall apart in another.
Good prompting is not about magic words. It is about understanding how models interpret instructions, examples, constraints, and ambiguity in real use. If you want better results from AI without guessing blindly, this category will help.
Browse the articles below to explore prompting in a deeper and more practical way.
Chain-of-thought prompting works by asking the model to reason step by step before producing an answer. For large models, this reliably improves performance on multi-step reasoning tasks. For small …
Continue Reading about Why Do Chain-of-Thought Prompts Sometimes Make Small Models Worse? →
Yes, partially and inconsistently. A system prompt can constrain tone, restrict topics, set a persona, and bias the model's behavior throughout the conversation. It cannot guarantee that every user …
Continue Reading about Can a System Prompt Quietly Override Everything You Say Later in the Chat? →
Temperature controls how the model samples from its probability distribution over possible next tokens. Low temperature makes the model pick high-probability tokens consistently. High temperature …
Continue Reading about Why Does the Same Prompt Get Smarter Results at Temperature 0.2 Than 0.8? →
