How to quickly craft perfect resumes for any job you want

Build a 'single source' CV, use AI tools strategically, and automate formatting — so you can tailor your resume for every role in a fraction of the time.

Career
December 9, 2024
·
13 min
read
Abstract brand form on a purple field

TL;DR

  • Tailor your resume for each job to enhance your appeal—in no time
  • Prepare a comprehensive ‘single source’ CV in Google Drive for efficient, automated applications
  • Optimize your LinkedIn profile for AI-driven job recommendations
  • Show your personality and network, network, network

Two hard and fast rules of job searches

In my career, I’ve followed two crucial rules for job searching: always customize your resume for each position you’re genuinely interested in, and strive to be memorable. Customization gives you an edge in a crowded candidate pool, while a compelling personal narrative helps you stand out.

Each job hunt is an opportunity to refine your storytelling skills and make a lasting impact. Over the years, I’ve honed my approach to job searching by documenting the most effective resume-building and automation strategies. This guide offers actionable steps to streamline your job search:

  • Share my step-by-step automation process to automate your tailored CVs effortlessly using tools you already have—no budget required
  • How making personal connections through platforms like LinkedIn, WhatsApp, and even a good old-fashioned phone call can help you stand out in the hiring process

Get ready

Preparation is vital. This section outlines:

  • The importance of your LinkedIn profile
  • Sourcing your total experience and skills
  • Setting up your Google Drive for automation

Although it may take a few hours initially, this investment will enhance your applications immensely.

What you need to get started

You’ll need subscriptions to:

  • Google Docs
  • Google Sheets
  • A subscription with ChatGPT or any other alternative
  • Job position platforms
  • Preferably, a LinkedIn account and profile

All are free tools.

Optimizing job searches with LinkedIn AI

To make the most of LinkedIn’s AI-powered job recommendations, ensure your profile is up-to-date and polished. A well-maintained profile maximizes the effectiveness of this intelligent matching tool, which analyzes your profile, past applications, and network interactions to suggest job opportunities tailored to you.

Regularly updating your profile increases the chances of receiving relevant job recommendations. Engaging with your network further amplifies these benefits.

Make a single source CV

Create a comprehensive ‘single source’ CV that includes every relevant experience, responsibility, and achievement that you consider important for at least one of the roles you’re considering.

Organizing this document creates a robust foundation for crafting tailored resumes for specific applications, maximizing your chances of landing your desired opportunities. Keep this up-to-date—whenever you remember a skill you hadn’t added, and whenever you acquire a new skill, add it. Same for jobs.

Automation with Google Drive

Central to my job-hunting guide is maximizing efficiency through automation. I’ve created a comprehensive Google Docs CV template with fields automatically populated based on position-specific data. The template works in conjunction with a Google Sheets database.

Once you run the provided Google Apps Script, it:

  1. Reads the data from the third row in the spreadsheet
  2. Copies the Google Docs template into your designated Drive folder
  3. Replaces all placeholders with the specific role details, achievements, experiences, and skills from that row
  4. Generates a new CV document named to reflect the job title and the current date

To make the magic happen, you’ll need to follow a one-time setup.

Set up your environment

  1. Create a dedicated folder in your drive
  2. Copy the folder ID from the URL and save it
  3. Download the template file (includes a pre-programmed Apps Script)
  4. Store the copy in your new dedicated folder
  5. Rename the Google Sheets version to CVData

Then, from Google Sheets, go to Extensions > Apps Script. Using the values you copied and saved, replace the IDs inside the script:

  1. Replace the sheetId with the ID you saved from Google Sheets
  2. Replace the templateId with the Google Docs ID you saved
  3. Replace the folderId with the ID of the dedicated folder you created
  4. Replace yourname in YOURNAME_CV with your last name
  5. Save the script and close the scripts tab
  6. From the Google Sheets file, select File > Download > Comma Separated Values (.csv). You’ll need this when you work with ChatGPT.

Get set

Now it’s time to find a job you’re interested in and create a CV that aligns with that role specifically.

Analyze jobs that interest you

Start by diving into the details of job postings. Rather than skimming, break down each listing into core components: required skills, key responsibilities, and preferred qualifications.

For jobs on LinkedIn, utilize the automatic skills analysis feature. LinkedIn quickly compares your existing skills with those required for the positions you’re eyeing. If you notice a mismatch, use this opportunity to update your LinkedIn profile with relevant skills.

This matters for two reasons:

  • To stand out to recruiters who leverage LinkedIn for outreach campaigns
  • To stand out when applying for the specific job, especially if you share your profile link in your CV

Maximize AI tools for job matching

Leverage your free subscription to ChatGPT (or any other alternative). Engage in conversations about job skills and additional requirements to determine whether there’s a good fit between you and the role. You can also solicit feedback on how to approach your application.

Tailor content to each position

Initial estimated time: this might take an hour of conversation the first time, but as ChatGPT understands you better, the process per job continues to shorten.

Estimated ongoing time per job: 30 minutes

This used to take somewhere between 2.5-5 hours for every job—promise!

After thoroughly understanding the requirements, map your relevant experiences and achievements to match the job description using ChatGPT.

No need to pay for resume sites no matter how advanced they claim to be. You can achieve the same results for free if you follow this process.

ChatGPT learns about you over time. I find it easier to always use the same chat for all job searches—it learns and remembers feedback, working faster and more accurately as you progress.

Start chatting:

  1. Write in the chat: “I want to apply for the following job.” Don’t press Enter yet.
  2. Copy the job description and paste it into the chat, or share the job link.
  3. Write “Don’t respond yet — I’m sending more information.”
  4. Press Enter.
  5. If this is a new chat, attach your “single source” CV as well. If you can’t attach it, write “following are my credentials” and paste the text. Don’t press Enter yet.
  6. Write “Please write my CV for this job.”
  7. Now, press Enter.

ChatGPT automatically incorporates strategic keywords from the posting, which are crucial for passing through Applicant Tracking Systems (ATS).

Each resume gets precisely calibrated to match specific role requirements through carefully crafted position summaries, highlighted relevant achievements, and emphasized key competencies.

Refine the wording

While AI tools are powerful, it’s essential to maintain your personal touch. Read the output and look out for:

  • Missed points that you believe are critical
  • Unique use cases that might require a slightly different approach

Continue the discussion by reviewing the CV output. Repeat the cycle until you’re 80-85% satisfied with the outcome.

Note: the chatbot might sometimes hallucinate. Don’t argue—just leave those mistakes and handle them manually later.

Automate Formatting

Estimated time per job application: 5-10 minutes

Get a CSV of your custom CV

Ask ChatGPT to format the customized content as a CSV file with your job data organized in the proper column order. For subsequent requests, you can write: “Please format my CV now as a downloadable CSV in the same manner as previously.”

Import the CSV to Google Sheets

  1. Open the Google Sheets you set up earlier
  2. Put your cursor on cell A3
  3. Insert a new row above (creating an empty row 3)
  4. Make sure your cursor is on A3
  5. Select File > Import
  6. Choose and import the CSV you saved from ChatGPT
  7. When prompted, replace data in the selected cell and click Import data
  8. Double check that everything imported correctly

Then click the New CV button. When the CV is ready, a dialog box will pop up showing you exactly what it’s been named and what folder it’s in.

Automate and finalize the custom CV

Now it’s time for some manual work. The bulleted description for Experience might not actually have bullets—this is a 10-second fix. Go to the end of the first bullet, press Delete once and Enter once. Repeat for every line.

Once you’ve completed the automation and the quick formatting fix:

  • Decide on education and language placement: the template includes both sections twice for convenience. Decide which version appears more nicely and delete the other.
  • Eliminate Errors: Thoroughly proofread to catch any typos, grammatical errors, or inconsistencies.
  • Remove Hallucinations and Verify Information: Carefully review the generated content for any fabricated information. Double-check all dates, job titles, company names, and other factual details.
  • Remove Unnecessary Info: When you’ve finished making corrections, delete all unused template details.
  • Align Formatting Tools: Make sure formatting is consistent. Use the paintbrush to fix things up if necessary.

Write the cover letter

A well-crafted cover letter can truly set you apart. It offers a unique opportunity to convey your enthusiasm for the position and showcase your personality in a way that a resume simply can’t. A couple of thoughtful paragraphs can highlight your relevant experiences and express your passion for the role.

Here’s what I recommend:

  • Keep your writing concise. Aim to limit the length to ensure clarity and maintain the reader’s attention.
  • Don’t reiterate your experience unless there’s a specific accomplishment that distinguishes you in relation to the role, and only then to add the relevant detail.
  • Focus on genuine excitement for the opportunity. Keep it light—avoid overly formal language, as it can sound robotic.
  • Let your unique voice shine through. Share insights or anecdotes that reflect who you are and how you’ll fit into the company culture.
  • If the application allows a personal note, consider using that option. It typically requires less preparation and offers a more informal way to connect with the hiring team.

Go!

Now it’s time to submit your CV in the most optimized way possible and to start making connections.

Network, network, network

Before submitting your CV, reach out to your network and explore how they can assist you. Connecting with friends and acquaintances might reveal referral programs or “friends bringing friends” hiring promotions. A simple message like “Hey, do you know anyone at XYZ company?” can lead to invaluable guidance and support.

Submit the application, network, track, and iterate

Once you’re all set to apply, make sure you’ve carefully followed all the details outlined in the job posting. Employers love detail-oriented candidates, so double-checking that you’ve ticked all the boxes is a smart move.

Make contact

After submitting your application, reach out to the hiring manager. If their name isn’t indicated in the job description:

  1. Visit the company’s LinkedIn page and navigate to the “People” tab. Search for your discipline to identify potential hiring managers.
  2. Review the company’s website, particularly the “About Us” or “People” section.

Once you’ve identified the hiring manager, send them a connection request with a personalized note. For example: “Hi [Name], nice to meet you! I’m Rachel, an enthusiastic Director of PMM with a passion for building from the ground up. I recently submitted my CV and would love to connect!”

Use the tone that feels right to you. For example, I once wrote to a manager: “WOW! I’m SO excited to hear about the opening at XYZ. Looking forward to hearing from you about the hiring process.”

Engaging with current employees

If you need help identifying the right person, consider reaching out to other professionals in related roles within the company. You might request an informational interview or seek guidance on navigating the company culture: “Hello [Name], I noticed we have similar professional backgrounds, and I would appreciate the opportunity to learn about your experience at [Company]. Would you be available for a brief conversation?”

When communicating with current employees, it’s beneficial to have specific questions prepared. Ask about their experiences, company dynamics, or tips for navigating the application process: “Could you share what aspects of [Company] you find most valuable and any guidance you might offer to a prospective applicant?”

Reach out to the recruiter or job poster

If a recruiter or job poster is identified in the job listing, following up with them is also advisable—especially if you have no updates after a couple of weeks. Keep your message friendly yet brief: “Hi [Recruiter’s Name], I am reaching out to follow up on my application for [Job Title]. I would appreciate any updates you could provide regarding my application status.”

Track your progress

Maintaining organization is essential. The Google spreadsheet documents the application date and job title. Add a status column to track your progress and stay organized for follow-ups. Also note any interactions you have with company representatives.

Follow up and stay in the loop

After completing your interview, adopt a proactive approach while waiting for feedback. A thoughtfully crafted message can effectively express your enthusiasm while serving as a gentle reminder of your candidacy:

“I hope this message finds you well. I wanted to check in regarding the status of my application for the [Job Title] position. I remain very enthusiastic about the opportunity to contribute to [Company Name] and would love to hear about any updates you might have.”

It’s also worth acknowledging that interviews can be overwhelming. Taking time to reflect after the interview is valuable. It’s completely acceptable to reach out later with questions or insights that arise from your reflections—and including them in a follow-up email further demonstrates your professionalism.

Also maintain an open line of communication with the recruiter. A brief follow-up after a couple of weeks: “Hi [Name], I hope you are well. I wanted to touch base regarding my application status for [Job Title]. Thank you for your time.”

Get feedback and iterate

If you experience a rejection, consider reaching out for feedback. Use their input along with your own reflections to improve your approach for future applications. Maintaining a forward-looking perspective can help you advance your job search.

You can also ask trusted friends, family members, or career professionals to review your resume and provide constructive feedback.

Conclusion

Leveraging technology to enhance your CV and utilize data insights can make your job search more efficient and increase your chances of landing interviews. Staying organized and using tools and methods like those I’ve discussed will allow you to tailor your resume for each position, giving you a competitive edge. By combining automation with your personal voice and experiences, you can save time and boost your appeal to employers. Get ready to showcase your skills and pursue great opportunities.

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Data Augmentation
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workflow
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The ordered steps and hand-offs by which work moves from start to finish, who does each one and what triggers the next.
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verification
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Confirming that a result, label or component is correct by checking it against a reference or a second method.
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Running several parts of a program at the same time on separate threads so work overlaps instead of waiting in line.
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Service Level Agreement: the committed level of service a provider promises, such as uptime, response time or support hours, with consequences if missed.
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A set of items grouped for a human reviewer to check in one sitting, often chosen because the model was unsure about them.
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Relational Database
A database that stores data in tables with rows and columns and links them through keys, queried with SQL.
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A saved, repeatable set of processing steps or settings that can be rerun on new data to get the same kind of result.
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Retrieval-Augmented Generation: a language model that first fetches relevant documents and then writes its answer from them, which cuts hallucination.
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In reinforcement learning, the strategy that maps each state to the action the agent should take.
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Multi-Version Concurrency Control: a database technique that keeps multiple versions of a row so readers and writers do not block each other.
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Continuously watching a deployed system's health and output quality so drift, errors and slowdowns are caught early.
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Model Versioning
Tracking each trained model with its data, code and settings, so any version can be reproduced, compared or rolled back.
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The numbers used to judge a model or system: accuracy, precision, recall, F1, latency and others chosen to match the goal.
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The formula that scores how wrong a model's predictions are during training; training works to make this number as small as possible.
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A hidden characteristic a model learns internally that is not directly observed in the data but helps explain it.
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Intersection over Union: the overlap between a predicted box or mask and the true one, divided by their combined area. 1.0 is a perfect match.
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Inverted Index
An index that maps each term to the documents containing it, which is how full-text search answers queries quickly.
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One individual occurrence of an object in an image, as distinct from the class it belongs to.
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Information Extraction
Pulling structured facts such as names, dates, amounts or relationships out of unstructured text or images.
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Indexing
Building a lookup structure over data so searches find matches without scanning everything.
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Image Processing
Operations applied to pixels, such as resizing, filtering, thresholding or colour correction, usually to prepare images for analysis.
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Hyperparameter selection
Choosing the settings that control how a model learns, such as learning rate, batch size or network depth, which are set before training rather than learned.
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Trying every combination of chosen hyperparameter values in turn and keeping the set that scores best.
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The direction and size of change in a model's error for each weight, used during training to decide how to adjust that weight.
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Graphics Processing Unit: a chip built for many parallel calculations at once, which makes it the workhorse for training and running neural networks.
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AI that creates new content such as text, images, code or audio from a prompt, learned from patterns in large training sets.
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A model's ability to perform well on data it has never seen, rather than only on its training examples.
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A cycle in which a system's outputs, or human reactions to them, are fed back as training signal so it improves over time.
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Creating or transforming input variables so a model can learn from them more easily, using domain knowledge rather than raw data alone.
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A model behaving without systematic disadvantage to groups defined by traits such as gender, age or region.
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The trade-off in learning systems between trying new actions to discover better options and repeating actions already known to work.
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Measuring how well a model performs against held-out data using agreed metrics, before and after it is deployed.
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When the data a model meets in production differs from the data it was trained on, so accuracy drops even though nothing in the model changed.
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A database's ability to stay available, correct and recoverable under load, failure and change.
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How fit data is for its purpose: accurate, complete, consistent, current and free of duplicates, noise and wrong labels.
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The record of where data came from and every transformation it went through, so any result can be traced back to its source.
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When information from the test set, or from the future, slips into training, so the model scores well in the lab and fails in production.
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A store that keeps raw data in its original format at any scale, structured or not, for later processing and analysis.
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Data that is complete, accurate and unaltered over its whole life, protected from corruption, loss or unauthorised change.
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data-ingestion
Data Ingestion
Bringing data from its sources into a system where it can be stored, processed and analysed, whether in batches or as a live stream.
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Data Governance
The policies, roles and controls that decide who can access data, how it is kept accurate and how its use is tracked.
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Data Consistency
Data that stays correct and in agreement across copies, transactions and systems, so every reader sees the same truth.
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Data Balance
How evenly examples are spread across classes or conditions in a dataset; imbalance teaches a model to favour whatever it sees most.
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cross-validation
Cross-validation
Evaluating a model by splitting the data into several folds, training on some and testing on the rest in turn, to get a more honest accuracy estimate.
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Cosine Similarity
A score between -1 and 1 for how closely two vectors point in the same direction, used to compare embeddings regardless of their length.
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Convolutional Neural Network (CNN)
A neural network built for images: it slides small filters over the picture to detect edges, textures and shapes, then combines them into higher-level features.
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Connected Components
Groups of items linked by similarity so that every member connects to the group through at least one close neighbour, used to find clusters and duplicates.
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Meeting the laws, standards and internal policies that govern how data is collected, stored, used and audited.
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Renting computing resources such as servers, storage and databases over the internet, scaling up or down on demand instead of owning hardware.
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Cloud AI
Running AI training or inference on a provider's infrastructure rather than your own hardware, paying for compute as you use it.
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The measurable benefit a project delivers to the organisation: revenue, cost saved, risk reduced or time recovered.
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Black-box Model
A model whose internal reasoning cannot be inspected or explained, so you can see what it predicts but not why.
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Big Data
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