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by hliyan
11 days ago
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I can provide a data point for what the article calls pseudo productivity: I extensively use LLMs as semantic search engines or expert systems (but not as agents). Recently I asked one how to consume a Google Pub/Sub topic using Python (context: I come from an C++/Java/JS background with some Python knowledge). The LLM gave me a good intro and some code. As it usually happens, I had a few follow up questions/clarifications, and then had to clarify the intent behind the code I requested. After a few relatively effortless rounds of back and forth, I got what I needed. It felt productive. But looking at the clock, about 20 minutes had passed without me even realizing it. Then I went and looked at the official overview page for the Google Pub/Sub Python client. It had everything I needed (including the code), in a more condensed, well-structured form. I could probably have arrived at the same outcome in 5-10 minutes. The only difference was that the latter method required some focus/discipline. I'm wondering whether this is what they call pseudo-productivity: a lot of low-friction back and forth that feels productive, and perhaps even enjoyable, but in objective terms, takes longer? |
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What I want to say is that it's very situational and it's likely good to focus on the average. Using LLMs as docs are bad when good docs exist, but if you aren't sure if they do, it's a gamble. A much better approach would be to have somebody pre-create and edit the docs with an LLM for each service with bad docs.
Only when your situation isn't covered would it make sense to create new docs.