#AI#GenerativeAI#LLMs#PromptEngineering#TechnicalWriting#SoftwareDocumentation#Productivity: "Many tech writers have a constant fear that AI will take our jobs. I often think, what I’m doing isn’t rocket science. Any person with some education can do it. And yet, just as engineers struggle to write, tech writers frequently struggle with AI tools. They don’t understand how to use them effectively. Even though “prompt engineering” is often a ridiculed term online, again and again I hear feedback from TWs about AI not being useful to them, or they simply don’t have interest in AI, as if it’s irrelevant to their work. This blows me away. When I can ramp up on a product in an hour and write a user guide in a couple of days, and code a doc publishing script that automates even more tasks, how can AI not be useful? How can it not be essential?
An often repeated saying is that AI tools won’t replace us, we’ll be replaced by those who know how to use AI tools. I feel like this is more and more true. Consider this scenario: You hire a roofer to install a new roof, which mainly involves removing the old shingles and installing new ones. One roofer arrives with a hammer. It will take this roofer 2 weeks to do the job. Another roofer arrives with a pneumatic roofing nailer power tool. It will take this roofer 3 days to do the job. The cost of the first roofer is 4 times that of the second. The output is pretty much the same. Which roofer do you hire?
It’s the same with tech writers. Suppose you have a large project. One tech writer can create the documentation using AI tools in a quarter of the time, while the other will take 75% longer. Which tech writer do you hire?
Fortunately, I think tech writers can learn how to use AI tools as power tools. Especially with more awareness and knowledge about effective prompting techniques, tech writers can become much more productive using AI." https://idratherbewriting.com/blog/ai-is-accelerating-me
I just realized that #AI “prompt engineering” is just SEO (“#SearchEngine optimization”) in reverse.
#SEO spams unique language into specific content in hopes of raising the latter’s rank when querying a corpus indexed by an opaque non-deterministic algorithm.
#PromptEngineering spams unique language into a query against a corpus indexed by an opaque non-deterministic algorithm in hopes of returning specific content.
“However, new research suggests that prompt engineering is best done by the model itself, and not by a human engineer. This has cast doubt on prompt engineering’s future—and increased suspicions that a fair portion of prompt-engineering jobs may be a passing fad, at least as the field is currently imagined.”
Conventional wisdom holds AI can't write anything personal or original. In today's post (Magnifying Ideas and Expanding Text with AI) I do just that, showing you how to get AI to write something both personal and original. https://sadlynothavocdinosaur.com/posts/magnify/
"Aftermarket" fixes applied after training, like injecting diversity terms into prompts, don't fix the underlying model and can even exacerbate harmful fabrications. If the training set is biased—and the Internet is—it's really hard to correct that after the fact.
As a teacher, I've come to understand the utility of new tech isn't always obvious until it becomes ubiquitous, until you have a chance to truly play with it. "Why on earth would I want a video projector in my classroom?" I thought at the turn of the century. Now, I couldn't live w/o it because it turned the "teacher's computer" into a "classroom computer." That's why I'm playing with LLMs, to discover the possible. You should join me. https://sadlynothavocdinosaur.com/posts/tldr-post/
I'm really proud of this browser extension—LIT Prompts.¹
It's kind of an "everything extension," letting you create your own AI-driven actions: summarize & query a webpage, extract data from a page, translate text, shorten text, build sims...
It's my answer to the question, "what tools would we need to take #promptEngineering seriously?" Basically, it's an #LLM-agnostic #AI playground for the coding curious.
cool #LLM trick — you want the LLM to process a chunk of text, but the text probably has pronouns that need context, so you give it 10x more text than it actually needs, just to retain context
solution: use #neuralcoref to replace ambiguous pronouns with their actual names. Send only the snippet that the LLM needs
I’m really enjoying all the things I can do with #LLMs, and #GTP4 is fantastically powerful.
But…
I'm getting increasingly frustrated with the text it generates. Feels to me like it was trained exclusively on marketing copy! No matter how "clever" I am with the prompts, I cannot reliably prevent it from leaning hard into hyperbole and SEO-friendly word vomit.
Anyone else tackled this? Any clever #promptEngineering I can use to make it less obnoxious? 🤷
“Prompt engineering” is such a bizarre line of work. You’re trying to convince a machine trained on a huge pile of (hopefully) human-generated text to produce some useful output by guessing what sequence of human-like words you must put in to make it likely that the model will produce coherent, human-like output that is good enough to pass downstream.
You really have no idea how your prompt caused the model to produce its output (yes, you understand its process, but not the actual factors that contribute to its decisions). If the output happens to be good, you still have no idea how far you can push your input before the model returns bad output.
Prompt engineers talk to the model like a human, because that’s the only mental model they have for predicting how it will respond to their inputs. It’s a very poor metaphor for programming, but there is nothing better to reach for.
Just finished reading Promptbreeder. An interesting idea oddly explained and executed.
tldr; if you can define a fitness function for prompts you can use LLMs to mutate and crossover a set of prompts to slowly evolve better performing ones.
Anyone have any good tricks for getting AI image generation models like #dalle or #stablediffusion to produce animals or people with three eyes? I was hoping to get a lemur with a typical “mind’s eye” third eye, but all the models seem to ignore the third eye condition no matter how frequently I specify it.
#AI#PromptEngineering is so depressing!The most popular #LLMs are intentionally designed to be as opaque as possible. But rather than demanding transparency and accountability, people are signing on to what is basically pseudoscience.
I get why it feels fun and scientific! That’s the whole point of pseudoscience — it gives you something to think about, so you can avoid the cognitive dissonance that comes from asking if the entire enterprise is harmful toxic bullshit
I tried the Dall-e prompt of "A realistic painting in the style of Gustave Courbet of the character Loki walking down a road while listening to a boombox."
I can't tell if the prompt is shit or if it is #Dalle's fault.
Mona-openai is a new Python package by mona that enables capturing logs to monitor your OpenAI API usage 🚀. That includes cool features such as:
✅ Hallucination alerts
✅ Tokens usage
✅ Behavioral drifts and anomalies
✅ LangChain support
Maximize ChatGPT Efficiency Without the Need for Expensive Tools or Complicated Prompting SchemesTired of being bombarded with countless prompts, so-called “advanced” meta prompting techniques, and expensive AI tools? It's all BULLSHIT. Say goodbye to complexity and hello to simplicity with our carefully curated collection...
Prompt Engineering: How to Think Like an AI - Tim Bornholdt (timbornholdt.com)
Tim Bornholdt is an entrepreneur, software developer, and podcaster based in Minneapolis, MN.
OC The Ultimate 5 ChatGPT Prompts: Simplify Your AI Experience (ngmi.gumroad.com)
Maximize ChatGPT Efficiency Without the Need for Expensive Tools or Complicated Prompting SchemesTired of being bombarded with countless prompts, so-called “advanced” meta prompting techniques, and expensive AI tools? It's all BULLSHIT. Say goodbye to complexity and hello to simplicity with our carefully curated collection...