Tag: LLMs

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Gardening ruminations

The SEO garbage search result that sent me over the edge

This post is a spiritual successor to LLMs are good coders, useless writers.

After getting steadily worse for years, the experience of searching the web just hit a new all-time low. I clicked on the top non-ad search result and encountered the worst word-salad nonsense I’ve ever seen. It was too perfect not to share.

I had let the small patch of lawn in my yard get knee-high. My reel mower can’t cut grass that tall, so I broke out the old weed whacker I got on the cheap at ShareHouse. It immediately ran out of the cutting string that it came with, so I found myself at the hardware store shopping for a refill.

I didn’t know if I should replace only the string or swap out the whole head. So, standing in Lowe’s, I whipped out my phone and searched it up: “restring toro weed trimmer”

(I winced when my kids started saying “search it up,” but I’ve since come to appreciate it. It avoids centering a corporation, unlike “I Googled it.” And I wasn’t using Google: the DuckDuckGo browser on my phone is, sadly, Microsoft Bing in disguise).

The first non-video result was from “Backyard Lord.” I’ve included screenshots in case that link dies, as it sure ought to.

Looking at it now, the “Pro Tips for Easy Trimming” suffix reeks of LLM garbage, as does the domain “Backyard Lord.” But the listed steps seemed like what I wanted. I clicked on it.

The page started off okay:

But that was the end of the plausible content. The next block was just keywords and mentioned a tennis racket??

The next block contained the prompt for the LLM! All highlighting mine:

From there it becomes free-association insanity. There’s a step-by-step guide … but each step discusses a totally different industry! Step 1, preparation, is about starting a business:

Step 2 is about restringing a guitar:

Step 3 is empty and Step 4 drips with irony as it talks about strings in the context of LLMs:

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Categories
ruminations Software Work Writing

LLMs are good coders, useless writers

My writer friends say Large Language Models (LLMs) like ChatGPT and Bard are overhyped and useless. Software developer friends say they’re a valuable tool, so much so that some pay out-of-pocket for ChatGPT Plus. They’re both correct: the writing they spew is pointless at best, pernicious at worst. … and coding with them has become an exciting part of my job as a data analyst.

Here I share a few concrete examples where they’ve shined for me at work and ruminate on why they’re good at coding but of limited use in writing. Compared to the general public, computer programmers are much more convinced of the potential of so-called Generative AI models. Perhaps these examples will help explain that difference.

Example 1: Finding a typo in my code

I was getting a generic error message from running this command, something whose Google results were not helpful. My prompt to Bard:

What’s the matter with this Python dict definition?

details = {
‘electrification_lvl’: decoded_vin.get(‘ElectrificationLevel’, None),
‘engine_kw’ = decoded_vin.get(‘EngineKW’, None),
‘fuel_type_primary’ = decoded_vin.get(‘FuelTypePrimary’, None),
‘fuel_type_secondary’ = decoded_vin.get(‘FuelTypeSecondary’, None),
‘battery_kwh’ = decoded_vin.get(‘BatteryKWh’, None)}

Bard told me I had a “significant issue”:

“You used an assignment operator (=) instead of a colon (:) to define the key-value pairs. This will cause a syntax error because assignment statements are not allowed within dictionary definitions.”

Yep! So trivial, but I wasn’t seeing it. It also suggested a styling change and, conveniently, gave me back the fixed code so that I could copy-paste it instead of correcting my typos. Here the LLM was able to work with my unique situation when StackOverflow and web searches were not helping. I like that the LLM can audit my code.

Example 2: Writing a SQL query

Today I started writing a query to check an assumption about my data. I could see that in translating my thoughts directly to code, I was getting long-winded, already on my third CTE (common table expression). There had to be a simpler way. I described my problem to Bard and it delivered.

My prompt:

I have a table in SQL Server called EV_VINs_Presence. It contains columns “vin”, “snapshot_date”, and “locale”. Every record that has a locale value of “Ann Arbor” should have a corresponding record with the same “vin” and “snapshot_date” but with the locale value of “State”.

Write a SQL Server query to return any records that do not have such a corresponding record.

Bard replied:

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