🤖 AI Prompt Pack - Talent Acquisition
Prompt topics covering the talent acquisition lifecycle.
At its essence, Talent Acquisition is the practice of attracting workers to a business and hiring those that are the best fit.
Sounds simple doesn’t it.
The AIHR have published an accessible guide on Talent Acquisition, and that’s where I’ve borrowed the image below-
Whilst covering the practice of attracting workers, and hiring those that are a fit, this image illustrates the various skills required of those working in the talent acquisition field.
In this prompt pack, three domains of talent acquisition will be focused on:
Attraction;
Selection, and
Metrics
In this prompt pack, you’ll learn:
How to setup your own prompts that are relevant to your context;
How to create a relevant synthetic persona to represent different stakeholders, and
How to use the personas to augment the quality and relevance of your AI interactions.
As usual, I’m not going to be prescriptive about what AI model you should use. Use what you have access to, what you’re comfortable with, and the model that has the features you need.
✏️ Fundamentals for writing great prompts
Give the AI a specific role and specific goal. For example, “You’re a Human Resources Business Partner with 10-years’ experience across multiple industries. Your goal is to find insights from process data.”
List step-by-step instructions, where necessary.
Share examples. For example, “An example of a previously successful business argument is …”
Ask for variations. For example, “That was a good effort but it hasn’t hit the mark. Please try again with more focus on …”. Feel free to have a conversation with the AI model.
Add constraints. For example, “provide a list of five KPIs”.
💡 You’re encouraged to play around with these prompts - your unique context will generate more suitable responses.
🗈 Note - Different AI models have different token sizes. The token size of an AI model represents the amount of information that it can consume, the amount of context it can hold, about a query. A rough rule-of-thumb is that 1,000 tokens equates to around 750 words. The larger the token size, the more context the AI model can handle.
💼 Case One – Attraction
Prompt one:
I am a Talent Acquisition Specialist with 15 years’ experience at a global publicly listed mining company and we are struggling to fill the role of Senior Mine Engineer.
Imagine a brainstorming session about talent acquisition challenges and opportunities in the mining sector.
This brainstorming session’s purpose is to come up with novel, clever and high-impact ways of attracting qualified mining engineers to our business.
The participants for this imaginary brainstorming session will be Human Resources guru Dave Ulrich professor of business at the Ross School of Business University of Michigan and co-founder of The RBL Group, famous non-executive director and No. 12 on Forbes list of the Most Influential Women in Media Arianna Huffington, innovative venture capitalist and frequent Forbes Midas List member Bill Gurley, and openAI founder and entrepreneur Sam Altman. The imaginary brainstorming session will be chaired by a global partner at McKinsey.
Please present me with a transcript of their provocative, high-energy and productive exchange and conclude by presenting their three most profound insights and recommendations on how to fill this role.
Example response one:
Absolutely! Here's a look into that high-powered brainstorming session with a focus on innovative solutions for attracting senior mine engineers:
Setting the Stage
The setting is a sleek minimalist conference room within McKinsey's offices. The atmosphere buzzes with anticipation.
McKinsey Partner: "Welcome everyone, and thank you for joining us to address this critical talent challenge. Our focus today is finding groundbreaking ways to attract world-class Senior Mine Engineers. With that, let's dive right in. Dave Ulrich, you've built a reputation on transformative HR strategies – where do you see opportunities?"
Dave Ulrich: "The mining industry needs a major rebrand. It's time to ditch the 'dirty and dangerous' image. We must showcase the cutting-edge technology, sustainability focus, and the sheer problem-solving complexity that defines modern mining. This isn't just about jobs, it's about building the future."
Arianna Huffington: "Absolutely. This is a narrative challenge. We need compelling stories about the engineers who make this happen – the ones safeguarding the environment, powering innovation, and quite literally unearthing solutions for a sustainable world. Mining can be positioned as the ultimate challenge for impact-driven minds."
Bill Gurley: "I agree, but we also need to talk brass tacks. The compensation packages for high-caliber engineers need to blow competing sectors out of the water. Combine that with the compelling vision you're both outlining, and we'll have a much stronger case."
Sam Altman: "Don't underestimate the pull of solving major problems. We need to tap into the engineer's mindset. Frame the work around tackling global resource challenges, or even the extraterrestrial possibilities of mining beyond Earth. Appeal to that inherent drive to engineer solutions on a grand scale."
McKinsey Partner: "Excellent points. We're talking about a complete shift in how we position this role. But how do we reach these engineers effectively?"
Dave Ulrich: "Universities are an obvious start. Let's create high-impact internships and targeted scholarships that place students at the forefront of the innovation happening today. We need them to experience this world firsthand."
Bill Gurley: "And we can't ignore the power of online communities. The best engineers are plugged into specialized forums and groups. A targeted campaign on the right platforms could have a wider reach than traditional job boards."
Arianna Huffington: "Mentorship! Pair seasoned professionals with rising talent. This builds a sense of belonging in what can be a very insular industry. We're talking long-term pipeline development."
Sam Altman: "Let's think like engineers ourselves. How do we create 'open-source' recruitment models? Contests, simulations, anything that invites talent to demonstrate their skills in a relevant context. This could uncover hidden gems who might thrive outside standard hiring processes."
McKinsey Partner: "Alright, let's distill this into our most actionable insights to address this Senior Mine Engineer shortage."
Top Three Insights and Recommendations
The Power of Rebranding: Mining isn't just a job; it's world-building on a grand scale. Emphasize sustainability, technology, and the impact mine engineers have on the future.
Targeted Talent Outreach: Move beyond traditional routes. Partner with universities for early engagement, leverage online communities, and consider open-source recruitment for overlooked talent.
Invest in the Pipeline: Mentorship programs build long-term relationships while demonstrating the company's commitment to the next generation of engineers.
McKinsey Partner: "This has been incredibly fruitful. Thank you, everyone. Now, let's work with our client to turn these insights into a bold strategy that will attract the very best in the field."
Let me know if you'd like a deeper dive into any of these specific recommendations!
Prompt two:
Whilst a good first effort, this brainstorming is too superficial, dispassionate and disconnected from both the professional experiences and technical capabilities and skill sets of its participants. It’s also disconnected from the lived experience of Mining Engineers.
Present a richer, more nuanced, more sophisticated transcript and set of recommendations. Do this understanding that I have more 15 years experience as an expert in Talent Acquisition.
Example response two:
You're absolutely right – this deserves a deeper and more expert approach! Let's revamp this brainstorming session to bring in the nuanced perspectives, specific skillsets, and the lived realities of mining engineering.
Revised Setting
The conference room is subtly altered. Instead of a sterile boardroom, there are engineering schematics sketched on whiteboards, geological samples on a table, and a projection screen cycling through images of modern mines–clean, technologically sophisticated operations.
McKinsey Partner: "Welcome. Our challenge remains the same: attracting top-tier Senior Mine Engineers. But, with a room like this, let's dig deeper than surface-level solutions. Dave, with your HR expertise and focus on building organizational capability, where's our starting point?"
Dave Ulrich: "We're facing a fundamental disconnect. The public image of mining hasn't kept pace with the reality experienced by engineers. What these engineers do aligns perfectly with what Gen Z and Millennials seek – purpose-driven work, solving grand challenges. We need to bridge that perception gap."
Arianna Huffington: "Purpose is essential, but I'm thinking about the day-to-day. Mining is remote, sometimes harsh. We must acknowledge that while highlighting the intellectual stimulation, problem-solving complexity...engineers thrive on that. But what about lifestyle balance? We need to be honest AND show how the company addresses it."
Bill Gurley: "Remote work is now standard post-pandemic. Can this work for engineers? It won't be for everything, but are there roles we could 'virtualize'? Maybe a team in the city managing drone surveys, remote sensor data... that widens our talent pool and addresses lifestyle concerns."
Sam Altman: "Speaking of talent pools...are we missing entire demographics? Mining became less female-friendly over decades – what's the HARD data on women engineers? Could a targeted campaign focusing on infrastructure, scholarships, shatter that glass ceiling and tap into a neglected talent source?"
McKinsey Partner: "Fantastic! We're rethinking the problem itself, not just the recruitment process. But talent acquisition folks, like our specialist here, know finding people is one thing, getting them to say 'yes' is another."
[Your Name]: "Bill's hit the nail on the head. Competing with Silicon Valley on salary is tough. But there's the 'psychic paycheck' – engineers want autonomy, mastery, a sense of ownership. Can we build that into the job design itself? Less rigid hierarchies, more direct impact on project outcomes..."
Sam Altman: "Could a DAO-inspired model work? A decentralized engineering team focused on a specific challenge... they own the process, compensation linked to the solution. High risk, but that kind of groundbreaking structure gets attention."
McKinsey Partner: "Alright, let's consolidate what we have. It seems our top areas are..."
Top Three Insights & Recommendations
Shatter Stereotypes, Address Realities: Target the disconnect between public image and the modern engineer's experience. Highlight purpose alongside transparently addressing lifestyle balance.
Tech-Enable Flexibility: Explore "hybrid" engineering roles. Some work can be remote, opening up wider talent markets, addressing work-life concerns, and appealing to tech-savvy engineers.
Disrupt the Structures: Consider alternative working models (project-based teams, decentralized decision-making) to attract those seeking autonomy and direct impact. Embrace diverse talent pools previously excluded from the industry.
[Your Name]: "These aren't overnight fixes. But they'll attract engineers who wouldn't even consider a 'traditional' mining job. And THAT'S how we'll find the talent who can transform this company."
Note: I kept it general as I don't have your specific mining company experience to build on. Feel free to substitute examples, specific tech, or challenges unique to your business to make this feel even more authentic!
Prompt three:
Now present a transcript where they disagree about the reasons it’s difficult to attract Senior Mining Engineers and summarise the two key elements and aspects of that disagreement.
Example response three:
The Debate Heats Up: A Recruitment Rumble
The veneer of initial agreement cracks as the brainstorming session deepens. Let's delve into the heart of the disagreement:
McKinsey Partner: "Alright, everyone, we've got some great ideas for reframing the industry and attracting top talent. Now, let's dissect the reasons behind this shortage in the first place. Arianna, your thoughts?"
Arianna Huffington: "It's a culmination of factors. The long hours, the remote locations...work-life balance just isn't there for many senior engineers, especially those with families."
Bill Gurley: "Hold on a second. My money's on compensation. These are highly skilled professionals. We need to offer packages that outshine what other industries can offer."
Sam Altman: "Bill, compensation is important, but it's not everything. Look at the tech world. Top engineers chase not just money, but groundbreaking projects, a chance to shape the future. Does mining offer that same level of cutting-edge appeal?"
Dave Ulrich: "Bingo, Sam! The industry is seen as slow-moving, resistant to innovation. We need to showcase the incredible advancements happening in mining – automation, sustainability, robotics. These are the things that will excite talented engineers."
[Your Name]: "It's a two-pronged attack, right? We need to address the lifestyle concerns for senior engineers, many of whom are juggling families. But we also can't underestimate the yearning for professional challenge. Can we offer both?"
The room crackles with the energy of the debate.
McKinsey Partner: "Fascinating. So, it seems we have two key areas of disagreement. Arianna and [Your Name] are highlighting the work-life balance concerns, while Bill and Sam believe compensation and a lack of perceived innovation are the bigger culprits."
Let's summarize the two key aspects of the disagreement:
Disagreement Element 1: The Driving Force Behind the Shortage
Work-Life Balance vs. Compensation & Innovation: One viewpoint emphasizes the strain of long hours and remote locations, particularly for senior engineers with families. The other argues that competitive compensation and a lack of cutting-edge projects are the main deterrents.
Disagreement Element 2: Addressing the Challenge
Lifestyle vs. Project Appeal: One solution focuses on offering flexible work arrangements and acknowledging the importance of work-life balance. The other emphasizes showcasing the industry's advancements and the potential for impactful projects to attract ambitious engineers.
This disagreement highlights the multifaceted nature of the challenge. By acknowledging both perspectives, we can develop a more comprehensive recruitment strategy that addresses both the lifestyle needs and the professional aspirations of senior mining engineers.
💡 Devising a brainstorming session is my favourite way to interact with a conversational AI model. This way I can have multiple personas approaching a problem and get a detailed transcript of them attempting to solve it. By using an AI with a large number of tokens allows the whole brainstorming session to remain in context each time a follow-up question is asked.
💼 Case Two – Selection
Prompt one:
You are a well-regarded global CEO who worked your way into the C-Suite over your 45-year career. You started out as an apprentice in an underground gold mine, moved into a Mining Engineer role in a remote Mongolian copper mine, were promoted into a Senior Mining Engineer role based in an Australian capital city overseeing short-term mine planning and development, before moving into the Chief Mining Officer and then CEO role.
I am a talent acquisition specialist who has been working in mining talent for 15 years. I have an undergraduate degree in business, and a post-graduate degree in human resources management. I am not a mining engineer.
Imagine a mentoring session where you coach me through assessing the value that mining engineers bring to our global publicly listed mining company.
The purpose of this mentoring session is transfer skills to me so that I can add value whilst assisting hiring managers in selecting prospective Senior Mining Engineers.
Further context is we have decided to be innovative with out most recent attraction campaign as our traditional methods of sourcing mining engineers was not successful. We have recently run a Code-A-Thon for prospective Senior Mining Engineers to work together and solve a challenge with an open stope mine development that is currently afoot.
Please present a transcript of the provocative, high-energy and productive mentoring session.
Example response one:
Absolutely, let's dive into this mentoring session. Here's how our conversation might go:
Scene: Your spacious office with a window overlooking one of your mine sites. Geological samples and framed engineering plans decorate the space.
You: "Welcome, [Talent Acquisition Specialist]. Excited about this Code-A-Thon concept! But, before we talk recruitment, let's go to the source. Why do we spend millions on these engineers in the first place?"
[Talent Acquisition Specialist]: "Well, obviously, they're vital for planning, designing, the whole mine works... But I'll admit, I don't always get the nitty-gritty beyond resumes and interview notes."
You: "Perfect, that's where I want to start. Think of a Senior Mining Engineer like the architect of the operation, but instead of buildings, they design how to get the treasure out of the ground."
[Talent Acquisition Specialist]: "Treasure out of the ground... I like that! So, not just efficiency, but knowing where the good stuff even is?"
You: "Exactly! They start with geology. A bad engineer builds a perfect mine...in the wrong spot. They turn data – drill holes, soil samples – into 3D models of the deposit. That's the 'treasure map', but it's more than just finding it."
[Talent Acquisition Specialist]: "More? Like how to get it out safely and cheaply?"
You: "That's where it gets complex. Open pit versus underground, how deep, how wide... Every choice impacts literally tons of cost. A good engineer gives options – 'We can dig faster, but spend more. Or go slower, safer, but less profit this quarter'."
[Talent Acquisition Specialist]: "So...engineers impact the bottom line, not just production targets? That's...not how it usually gets explained."
You: "That's the recruiter in you talking! A senior engineer is a translator – geology into dollars. They work with finance, executives…they impact the stock price. But let's get specific to your Code-A-Thon."
[Talent Acquisition Specialist]: "Right. It's an open stope challenge. Teams have to design the layout, how to blast, where to put the support pillars…it's pretty technical."
You: "Excellent. Look beyond just who codes the fastest. Is someone making risky calls for a higher grade? That's a gambler, not an engineer. Is one team butting heads? Shows they can't collaborate with those they'll have to convince on site."
[Talent Acquisition Specialist]: "I see...we're also looking for decision-making, communication, not just raw skill. But what about the quiet ones?"
You: "Don't discount them! Some of the best engineers are introverts. Did one just quietly fix someone else's code? That's a team player under pressure. Engineers are puzzle solvers, the method is just as important as the solution."
[Talent Acquisition Specialist]: "This is shifting my whole perspective. Now I'm excited to watch this Code-A-Thon in a whole new way!"
You: "That's the goal. You won't become a mining engineer overnight, but this mindset gets you closer to the heart of what we need. Now, use this to craft those interview questions, those follow-ups with hiring managers..."
[Talent Acquisition Specialist]: "Got it. Focus on problem-solving, impact, and how they think, not just what they know…"
You: "You're starting to talk like an engineer yourself!"
Let me know if you want a follow-up session, maybe to analyze Code-A-Thon results, or prep interview questions together!
Prompt two:
This is a great first round. Now go into more detail on specific measures and metrics that the Talent Acquisition Specialist can assess candidates against.
Example response two:
Absolutely, let's dive deeper into the specific traits and metrics to focus on during your Code-a-Thon observation and beyond:
Metrics to Observe During the Code-a-Thon:
Iterative Approach: Strong engineers don't just stick with their first idea. Do you see them revisiting their solutions, tweaking parameters, and re-running simulations? Shows adaptability and a pursuit of optimization.
Risk Assessment & Management: Mining is inherently risky. Look for those who discuss safety factors, incorporate contingencies, and show awareness of potential failure points in their design.
Cross-Functional Thinking: How do they integrate geological data? Do they consider downstream logistical constraints or the financial impact of their choices? This demonstrates holistic mine system understanding.
Collaboration & Communication: Observe discussions, disagreements, and how they reach consensus. Note if someone clearly explains their reasoning or helps a struggling team member. Soft skills are crucial for on-site success.
Interview Follow-ups (Based on Code-a-Thon Performance):
Thought Process over Final Solution: "Walk me through the key decisions you made during the challenge, and why you discarded certain options." This reveals their ability to prioritize variables and think under pressure.
Addressing Trade-offs: "Your design was high-risk. Talk me through your assessment of risks versus potential rewards." Uncovers their ability to balance safety with profitability.
Learning from Feedback: "If you could run the Code-a-Thon again, knowing what you know now, what would you do differently?" Demonstrates coachability and openness to improvement.
Beyond Technical Skills: General Questions for Interview
"Tell me about a time a mine plan had to significantly change due to unforeseen circumstances. How did you adapt?" This tests their agility and response to real-world challenges.
"Describe a situation where you had to persuade other stakeholders (geologists, executives, etc.) to adopt your engineering plan." Illustrates influence and communication skills.
Additional Tips:
Reference Checks: Speak with their previous colleagues from other mining projects to get insights on their work ethic, problem-solving in real-world situations, and collaborative style.
Behavioral Questions: Use scenarios to assess judgment calls: "A blast goes wrong. What are your first steps?" This reveals their safety mindset and crisis management ability.
Important Note: Remember, a Code-a-Thon is not a perfect predictor. Balance these insights with their broader resume, work history, and interview performance for the most comprehensive assessment.
Let me know if you'd like even more specific prompts for any of these categories, or if you want to role-play an interview scenario!
💼 Case Three – Metrics
Prompt one:
I am a manager for the talent acquisition function at a global publicly listed mining company and I am implementing a new analytics strategy for the talent acquisition function.
Our people analytics framework has nine dimensions: Governance, Methodology, Stakeholder Management, Skills, Technology, Data, Workforce Experiences, Business Outcomes, and Culture.
Imagine a brainstorming session about applying our people analytics framework across the talent acquisition function.
This imaginary brainstorming session’s purpose is to come up with novel, clever and high-impact ways of both better measuring and getting greater value from talent acquisition processes.
The participants for this imaginary brainstorming session will be Human Resources guru Dave Ulrich professor of business at the Ross School of Business University of Michigan and co-founder of The RBL Group; globally respected speaker, author, influencer, and business advisor in HR strategy and people analytics Jonathan Ferrar, award-winning writer, speaker, and executive consultant on people analytics and the future of work David Green; inventor, electrical engineer, computer programmer, and Apple co-founder Steve Wozniak; and professor of Operations, Information and Decisions at the Wharton School of Business University of Pennsylvania
. The imaginary brainstorming session will be chaired by a global partner at McKinsey.Please present a transcript of their provocative, high-energy and productive exchange and conclude by presenting their three most profound insights and recommendations.
Example response one:
Absolutely! Let's dive into this high-caliber talent analytics brainstorming session:
Setting the Stage
The setting is an innovation lab-like space, complete with whiteboards covered in data schematics, and a projection screen cycling through visualizations of workforce trends.
McKinsey Partner: "Welcome everyone. We're tasked with revolutionizing talent acquisition at a major mining firm. They've built a robust people analytics framework, but the focus now is measurement and driving value from within the TA function itself. Dave, with your HR strategy expertise, can you set the stage?"
Dave Ulrich: "Absolutely. The days of TA being just a cost center are over. We need to prove our work generates tangible business impact. But this isn't just about efficiency metrics, it's about aligning with the bigger picture of what this company is trying to achieve."
Jonathan Ferrar: "Right on, Dave. This is about making TA a data-driven strategic partner. We need metrics that measure our contribution to things like productivity, innovation, not just 'time to fill' and the usual."
Steve Wozniak: "Don't limit yourselves to what you CAN measure now. Think like engineers. Can you instrument your process? Put sensors in the candidate journey to see where we lose traction, where bias creeps in...the data might surprise you."
Kadambari Sridhar: "Building off of that, let's think long-term ROI. It's not just about the cost of a hire, but what that hire DOES. Do placements from a certain source outperform others? THAT's the data point your executives want."
🗈 Note - I have no idea how this name got in the mix. AI isn’t always accurate!
David Green: "Speaking of executives, stakeholder management is crucial. We need to translate this data into narratives they understand. Show them how a talent bottleneck delayed a project launch, costing X millions in revenue."
McKinsey Partner: "Excellent! We're moving from pure measurement to leveraging the data strategically. But how do we make this actionable on the daily grind for a TA team?"
Dave Ulrich: "Start with pain points. High turnover in a certain role? Deep dive into your data. Is it recruitment, onboarding, management...you might be solving the wrong problem!"
Jonathan Ferrar: "Skill gaps are a huge hidden cost. Can you tie your recruitment data to workforce models, and PREDICT what talent you'll need six months out? That's a huge win."
Woz: "Gamification. I'm serious. A dashboard where recruiters compete on metrics most tied to long-term impact, not just filling positions. Make the data fun."
McKinsey Partner: "Alright, let's solidify these with our top recommendations for this mining company:"
Top Three Insights & Recommendations
Instrument Your Process: Don't just measure outcomes, measure the journey. Identify bottlenecks, bias points, and areas for optimization within your existing workflows.
Connect Talent to Outcomes: Move beyond 'vanity metrics.' Demonstrate how improved TA processes link to increased productivity, reduced time-to-market, and overall bottom-line impact.
Data-Driven, Not Data-Drowned: Focus on actionable metrics tied to key pain points and long-term goals. Engage stakeholders with simple, compelling visualizations of this impact.
McKinsey Partner: "This framework offers a strong foundation, but to truly transform your TA function, remember: iterate, just as your engineers do. This is a continuous improvement journey."
Let me know if you'd like to brainstorm specific metrics or tools to implement any of these recommendations. With your mining context, we can make this even more tailored!
Prompt two:
Thank you but this doesn’t give me enough detail to work on. I need two things.
Firstly, I don’t recognise the participant Kadambari Sridhar in the imaginary brainstorming session. Who are they and what value are they bringing?
Secondly, the purpose of the imaginary brainstorming session is to come up with novel, clever and high-impact ways of both better measuring and getting greater value from talent acquisition processes. I need the McKinsey partner to itemise the outcomes of the brainstorming session against the nine dimensions of the people analytics framework that will provide greater value for the organisation.
Example response two:
Absolutely, let's address both of these points for a more tailored and actionable outcome:
Clarifying Kadambari Sridhar's Role
You're right to question this! It seems I've inadvertently included a real person. Let's adjust the participant list to better reflect the goals of the session:
Replacement for Kadambari Sridhar: Instead, we'll include a fictional Head of Mining Operations. This person brings deep operational knowledge, understands the link between talent gaps and mine productivity, and can champion data-driven talent solutions at the executive level.
Revised Participant Value
Dave Ulrich: Focuses on the big-picture strategic alignment of Talent Acquisition and business goals.
Jonathan Ferrar: Brings expertise in translating people analytics into actionable HR initiatives and communicating value to stakeholders.
David Green: Emphasizes the power of data storytelling for executive impact, ensuring TA is understood as a value driver.
Steve Wozniak: Provides unconventional, tech-driven insights on process optimization and unlocking hidden data points.
Head of Mining Operations: Links talent acquisition to operational goals, highlighting the real-world consequences of inefficient hiring.
Itemizing Outcomes against the People Analytics Framework
Let's break down potential recommendations from this session tied to each dimension of your company's framework:
Governance
Data ownership and decision-making: Define who owns which talent metrics, ensuring clear accountability and alignment with overall business goals.
Methodology
Standardized KPIs across functions: Collaborate with other HR functions to ensure consistent measurement (e.g., 'quality of hire' has the same definition for talent acquisition and performance management).
Stakeholder Management
Tailored reporting dashboards: Design visualizations for different stakeholders, highlighting metrics most relevant to their role (executives see business impact, line managers see pipeline health).
Skills
Upskilling for insight: Provide TA staff with training on basic data analysis and visualization tools to enhance their ability to use the information they gather.
Technology
Integrate with Existing Systems: Explore opportunities to integrate TA data with workforce planning models to predict future talent needs.
Data
Measuring the intangibles: Develop ways to quantify "candidate experience," a major driver of offer acceptance rates and employer branding.
Workforce Experiences
Retention risk prediction: Use exit interview data, and other factors to build a model predicting which new hires may be most at risk of early turnover.
Business Outcomes
Impact on Project Success: Track talent acquisition time versus project delays or cost overruns, showcasing the bottom-line impact of efficient hiring.
Culture
Diversity Metrics in Sourcing: Track the diversity of your candidate pools at each stage (applications, interviews, offers), identifying potential biases in the hiring process.
Important Note: These are just starting points! The specific metrics and actions you take will depend on your company's unique context and pain points.
Let me know if you'd like to brainstorm specific metrics or data visualization ideas for any of these dimensions!
📈 Level up
These prompts are designed to work across the variety of commercial GPTs without any specific configuration, and without needing to host and run your own models.
In a business setting, Simon recommends to ‘train’ your AI model on your relevant corporate data. If security is a concern then only use public data.
Imagine using your favourite GPT model, say OpenAI’s ChatGPT-4, but have it trained on public information about your company, and any relevant documents that you have from your company. This is not fantasy, this is available now and for relatively little effort.
How to do this?
Get started with AnythingLLM. AnythingLLM is a self-hosted AI chat bot that you can run on your personal desktop (Mac, Windows, and Linux) or in the cloud from source, in Docker, or via a subscription if you don’t have your own servers. It’s fully private, supports custom models, and can be ‘trained’ on documents or websites that you provide.
Choose the LLM that suits your context the best. You can use many of the popular commercial models in AnythingLLM including OpenAI, Microsoft Azure, Anthropic AI, Google Gemini etc. You can also download and install LocalAI to run open-source models from Hugging Face with AnythingLLM (although this does require a fair bit more disk space, and potentially GPU infrastructure).
Make sure that embedding selection suits your use case. This is the ‘context’ part where you embed documents and URLs in the AI model to provide better and more accurate context. AnythingLLM’s default ensures that a local open-source model is used so your data never leaves your instance, however models are available for OpenAI and Microsoft Azure, and for LocalAI if you want to run your own model.
AnythingLLM is shipped with a local vector database running on LanceDB. This will be fine for most applications, however you can use a number of other popular vector databases running alongside your AnythingLLM instance, or on other servers. For reference, a vector database is a collection of data stored as mathematical representations. Vector databases make it easier for machine learning models to remember previous inputs, allowing machine learning to be used to power search, recommendations, and text generation use-cases. Data can be identified based on similarity metrics instead of exact matches, making it possible for a computer model to understand data contextually.
Point AnythingLLM to your corporate website and upload documents relevant to the context that it’s operating in. You can have multiple workspaces if you need multiple contexts. With AnythingLLM having access to information about your company, its responses will be much more suitable. For example, if you’re creating a “talent acquisition” workspace you can enter the URL for your public corporate Employee Value Proposition page, and upload relevant documents such as corporate plans, annual reports, strategies, and recruitment-related policies. All queries via AnythingLLM will obtain responses with this corporate context.
Still skeptical about how simple this is?
Watch the video below where Tim Carambat, founder of AnythingLLM, sets up an AythingLLM instance in the cloud, integrates with OpenAI (ChatGPT), trains the instance on data from the public AnythingLLM website, and embeds a chatbot on a website, all in a matter of minutes with no code at all.
If you don’t need to run AnythingLLM on a server (e.g. you don’t need a chatbot embedded in a website) then download AnythingLLM to your computer and watch up to the 4:48 mark. Yep, a custom, contextual, local AI in less than five minutes.