How can LLMs write perfect code but not solve the same problem in conversation?

<!-- SC_OFF --><div class="md"><p>Something I keep noticing is that LLMs can give really strong, coherent answers in a single prompt, but as soon as the conversation goes on for a

<!-- SC_OFF --><div class="md"><p>I understand the basic principle (it looks at a vast array of data and uses probability to predict the next word)</p> <p>but how the hell is that

<!-- SC_OFF --><div class="md"><p>This is one of those simple tasks that you would expect them to be able to do given all the other more complicated stuff they are capable off,

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s1ckkm/why_llms_sound_right_but_fail_to_actually_do/"> <img alt="Why LLMs sound right but fail to actually

<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1srx4i1/new_llm_position_bias_benchmark_does_an_llm_keep/"> <img alt="New LLM Position Bias Benchmark: does

<!-- SC_OFF --><div class="md"><p>I build context/harness optimization tooling, so provider-side serialization quirks actually matter to me. If you're optimizing over prompts, you

<!-- SC_OFF --><div class="md"><p>Is there any local llm that gets close to Claude code in agentic coding?</p> </div><!-- SC_ON --> &#32; submitted by &#32; <a

<!-- SC_OFF --><div class="md"><p><a