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Powered by Hydejack v6.6.1<br>-->An Honest Review of AI Programming · Mathieu Ropert Jump to: Menu
It’s getting hard to avoid LLMs these days. Even if you avoid social media (or at least curate a follow list that avoids the bulk of the slop factory) and shrug off grandiose marketing statements that end up taken at face value in the news, it will likely come and find you at your place of work. Unlike the silver bullets of the past (like microservices or NoSQL), AI adoption seems to have been mandated in many places from the top layer of management, regardless of how many of them ever worked (or studied for) an engineering job.I do admit that this approach immediately triggered my contrarian side and made me very defiant of any AI tool. I don’t believe someone who has never written a line of code in their life should be telling me what to use for my engineering job. This sounds to me like the most terminal case of micro-management, and that’s never a good thing (on top of being personally insulting).Either way, over the past 3 months I got to use Claude and friends for work and I have to admit I found it somewhat useful. As long as you don’t ask it to write code. Please don’t ask it to write code. But I’m getting ahead of myself.Artificial “Intelligence”<br>You probably heard this a million time by now, but artificial intelligence really isn’t that intelligent. It’s all marketing and buzzwords. All we really we have here is a (very) large neural network specialized in natural language processing.As it turns out a lot of what we humans do on the computer is use text to communicate both ways, that model can be used to parse queries, generate textual1 responses based on a probabilistic heuristic or output command lines that can be then executed the old fashioned way and their results fed back into the model to make a loop until we reach some exit condition.That’s not to say this is inherently bad. But it’s not magical either. “Agentic workflow” (or whatever they’re calling it at the time you’re reading this article) is just the realization that every software problem can be solved by adding another layer of indirection, and LLMs are no exception. If the neural network output can be improved by providing more input, then attach more input. And if the best way to figure out which input that would be is to query the model to generate a command and then pipe it through system(), so be it.But let’s focus on the user point of view for the rest of this article.We have Google at home…<br>There’s tons of valuable stuff out there on the internet. And assuming we do a decent job at keeping existing records intact, the net total sum of public knowledge can only improve (but, spoiler warning, there’s a caveat). The problem is finding it. This isn’t a new thing. I’m old enough to remember the time when you’d first get suggested to try this new “google” thing. But sadly it’s gotten quite worse since the 2000s golden age. They have been fighting an uphill battle against SEO for a while now, and it doesn’t look like they’re winning2.Enter AI, as both a help and a hindrance. As a search tool, I have found it usually good at answering pointed questions expressed in natural language, especially given the conversational ability that allows refining the answer or bring up follow-up questions with the current context in mind. In practice it is the equivalent of running a bunch of searches, skimming through the top N links, and repeating until we think we’ve got the picture. Writing a short summary of a longer texts seems to be what LLMs are best at, and so it makes sense to use it to automate the process. Plus crawling and summarizing multiple searches is an inherently parallel job so it isn’t hard to see the efficiency that can be brought up by automating the process, assuming we have enough compute available and it doesn’t hallucinate the summaries, we’ll get back to both those points later because they are quite important.The hindrance counterpart is of course that LLMs have lowered the cost of flooding the internet with word salad that dilutes an already precarious sea of information. Freya Holmér published a very good video on the topic and impact of littering the web with AI generated content in the never ending SEO arms race. Searching for information by hand is still an option today, especially if you already know reputable sources on a particular topic. But if you’re not, then a lot of care must be taken to sort out the slop from the real data, and automating it with an LLM loop may not help if you can’t instruct it which sources to keep and which to discard. Even before we talk about hallucinations, an LLM generated summary can only be as good as the sources it ingested.…and now we also have Google at work<br>A particular area where I found an LLM loop useful for searching, that I haven’t found much discussion about so far, is internal company...