AI assisted job applications (and application screening)

 NBC news has the story:

AI is supposed to make applying to jobs easier — but it might be creating another problem
Companies have touted new AI technology that allows users to apply to thousands of jobs per day, flooding job openings with resumes.  By Sophia Pargas

"With the help of new AI software, applicants can now revise their resumes and cover letters, receive live interview scripts and launch chatbots to submit thousands of applications almost instantly.

...

"LazyApply, Simplify and AI Hawk are all AI services that launch assistants to help collect applicant information and submit automated job applications through sites like LinkedIn and Indeed. Using the tools, job seekers can save hundreds of hours — and apply to up to thousands of jobs a day, according to the services.

"AI Apply, which claims its users are “80% more likely to get hired,” offers tools like a cover letter and resume builder, an auto-apply feature, an interview practice generator and a specialized interview buddy. A premium membership starts at $38 a month, according to the website — a cost AI Apply notes is much lower and more accessible than the cost of a career counselor.

...

"According to the company’s respective websites, OfferGoose has helped applicants land over 17,000 job offers, and AI Apply has over 340,000 “freelancers and job seekers” using the service.

"Nevertheless, many companies have enlisted additional safeguards to detect the use of AI. Some employers now require e-confirmation codes to submit applications and prevent automated submissions. Others have added prompts asking AI bots to use specific words like “banana” in responses to catch chat-generated application answers, according to longtime job recruiter and tech agency director of talent Mike Peditto.

"But job seekers are not the only party using AI in the application process, according to a recent University of Washington study. The researchers estimated that “99% of Fortune 500 companies are already using some sort of AI assistance when making hiring decisions,” and found that resume-screening tools have an incredible bias toward white, male applicants — with the large language models favoring white-associated males roughly 85% of the time and disadvantaging Black males in “up to 100% of cases.”

"The use of AI by both applicants and employers has created a distinct dynamic, in which applicants are using AI, but are also reticent about employers screening them with AI."

Prompt Engineering, by Mukund Sundararajan

 Do you want to improve your chats with chatbots based on Large Language Models (LLMs)? (For example, GPT, Gemini, Anthropic/ Claude, Meta...) Here's a chance to learn from Mukund Sundararajan, who knows what those LLMs are good at (and not so good at) and why, and how to think about how to prompt them. His book might have been called How to be a Prompt Engineer.

Thinking Like A Large Language Model: Become an AI manager 
by Mukund Sundararajan
 
"Consider an analogy. You are a manager, you have an employee, and you are trying to get a task done. It helps to understand how the employee thinks, what the employee knows, and how they may behave. What would happen if you made a certain request? How would the employee interpret it? How would they respond? What do our past interactions with the employee tell us about their capabilities? How should we phrase our request so that it is interpreted correctly? Should we break our request into steps? What aspects of their work would we need to verify?

"Now imagine the employee is an AI. It still helps to have answers for the questions above. The goal of this book is to help you arrive at those answers.

"We describe how a Large Language Model (LLM) operates in three different ways: via input-output examples, via an overview of its training process, and via analogies to other familiar concepts.

"You don't have to work with technology to understand this book. It will be accessible to anyone with a high-school education—students, professionals, business-people, executives—anyone who wants to extract value out of LLMs or chatbots.

"Author details: Mukund Sundararajan is a Distinguished Research Scientist at Google DeepMind. He has spent the past decade analyzing, developing, and deploying AI in products—successfully and unsuccessfully. He has also observed others do so. He currently works on the Gemini series of Large Language Models and writes prompts for a living."