I remember the early 2000s, a time marked by heated discussions in Slashdot comments and perhaps even earlier on Usenet. The topic of interest? Articles presenting possible solutions to fight spam. Accompanying these articles, a popular checklist was often shared. Now, in the 2020s, we're seeing a similar phenomenon with articles questioning the usefulness of LLMs in software writing. So, I decided to dust off this old spam checklist and repurpose it to the modern age.

Your article critiquing using AI tools for software development is...

# AI Article Checklist

Your article critiquing using AI tools for software development is:

- [ ] ill-informed [ ] outdated [ ] biased [ ] shortsighted

and fails to persuade because of the following reasons. (One or more of the
following may apply to your particular critique, and it may have other flaws
that are specific to different LLMs or development contexts.)

- [ ] Relies on ChatGPT rather than exploring alternative, better-suited LLMs
- [ ] Misrepresents LLM capabilities by using single-shot examples
- [ ] Neglects the benefits of prompt iteration and refinement
- [ ] Disregards the value of experimenting with diverse prompt styles
- [ ] Overlooks LLMs as code review or debugging assistants
- [ ] Rephrases a problem in a way that removes all context leading to bad
- [ ] Uses prompt techniques that intentionally hamper the LLM algorithm (like
  "do not provide explanations")
- [ ] Ignores the potential of ReAct, chain of thought, or other
  prompt-enhancing techniques
- [ ] Omits crucial experiment details, hindering reproducibility
- [ ] Unfairly demands second-order explanations for first-order errors
- [ ] Fails to recognize that LLMs can benefit from standard software
  development practices (testing, specification, etc.)
- [ ] Underestimates LLMs' potential for generating pseudocode or high-level
  design outlines
- [ ] Dismisses LLMs as valuable documentation or tutorial creators
- [ ] Overlooks LLMs' potential in brainstorming, creative problem-solving, or
  idea generation
- [ ] Underappreciates the continuous improvement of LLMs with more training
  data and fine-tuning
- [ ] Ignores the value of LLMs for assisting novices or non-programmers in
  understanding code or creating simple scripts
- [ ] Obsesses over LLM-generated executable code, ignoring broader software
  development tasks where LLMs could shine

Additionally, your critique may face these philosophical objections:

- [ ] Similar critiques are easy to make, yet none have conclusively refuted the
  value of LLMs for software development
- [ ] Focusing on current limitations ignores the rapid advancements in LLM
- [ ] LLMs should be seen as complementary tools, not as replacements for human
- [ ] Collaboration between LLMs and humans can lead to innovative solutions and
  improved software quality
- [ ] LLMs can democratize access to programming knowledge and resources for a
  wider audience

Finally, this is what I think about your critique:

- [ ] Sorry, but your critique doesn't hold up against the potential benefits of
  LLMs in software development.
- [ ] This is a misguided critique, and you're missing the bigger picture.
- [ ] Nice try, but you're not going to derail the progress of LLMs in
  revolutionizing software development!
- [ ] This article could have been written by an AI tool.


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