Common Anti-Patterns to Avoid When Working with Large Language Models
Anti-patterns observed while working extensively with LLMs — from redundant context to over‑engineering.
Read the full articleYou might also wanna read
Context Engineering: The Real Work Behind Reliable LLM Systems
Most people who work with large language models spend their first year tuning prompts. They rewrite instructions, add “you are an expert,” t
Study reveals why in-context learning fails on complex specification-heavy tasks and how fine-tuning can help
In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations an
Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work
LLMs don’t fail because they forget—they fail because they remember too much. As conversations grow, prompts accumulate redundant and low-va
Study finds LLMs persist in treating false claims as true despite explicit warnings
Fine-tuning tests show "bias... toward confidently representing the claims as true."
arstechnica.com·1mo ago
Are Large Language Models a Large Research Problem?
There are use cases for LLMs in online research but behavioral scientists have growing concerns about the impact large language models might
Why Context Still Tricks Big Language Models
While large language models handle irrelevant context well in aggregate, specific instances reveal vulnerabilities. This inconsistency highl

Comments
Sign in to join the conversation.
No comments yet. Be the first.