Here is a list of templates, scripts, and prompts that can be used to effectively communicate with an AI assistant like myself when leveraging our capabilities for CV/cover letter customization:
Job Description Analysis Prompt: "Please analyze the following job description and identify the key requirements, responsibilities, and desired qualifications for this role..."
Resume/CV Submission Template: "I have attached my current resume/CV. Please review it thoroughly to understand my background, skills, experience, and accomplishments."
Career Goals/Focus Areas Script: "In terms of my career goals, I am primarily interested in [state your goals]. Please focus on highlighting my qualifications related to [list 2-3 key areas] when customizing my materials."
Initial Draft Feedback Prompt: "Thank you for the initial drafts. Here are my thoughts and areas I'd like you to revise/expand on..."
Clarifying Question Template: "I have a clarifying question about [specific section/experience]. Could you please elaborate more on [ask your question]?"
Revision Incorporation Check-In: "I've provided feedback on the previous drafts. Please confirm you've incorporated all my comments before proceeding."
Language/Tone Refinement Script: "For this application, the tone should be [formal/conversational/etc.]. Could you adjust the language accordingly throughout the documents?"
Formatting/Structure Feedback Prompt: "I would like to modify the formatting/structure. Here are my suggestions for improving the visual layout and content organization..."
Proofreading/Editing Request: "Before finalizing, could you please thoroughly proofread the documents and correct any spelling, grammar, or punctuation errors?"
Scenario-Based Customization Prompt: "I am [changing careers / networking / updating my resume] for [explain scenario]. Please customize my materials accordingly, highlighting [list key areas to focus on]."
By using these templates, scripts, and prompts, you can clearly communicate your needs and guide the AI assistant through the CV/cover letter customisation process. This will help ensure the AI generates materials that accurately reflect your qualifications and are tailored to your specific goals and target opportunities.
Make sure you have text only (to avoid copy/paste formatting problems) of your CV and advertised Job Description
Should it make any difference which AI Assistant to use for this process?
There are a few things that you need to know about AI Assistants, Large Language Models (LLMs), or General Pre-programmed Transformers (GPTs). These are all terms that loosely refer to the same solution - you know, one of those chatbot thingies that replies to your questions in a way that another person would.
The question is: Is there a best choice for this process? The answer is both yes and no.
When we built this training course we tested the process with a dozen different AI solutions.
At the time of writing all models learned comprehension of language from the same data source. If you have space, you can do this yourself. It is called the Common Crawl and contains all the publicly accessible text information on the Internet. This dataset is freely available to anyone - of course you'll need about 150Tb of space to download it, and over half a Pb to expand it into... but it is doable.
What this means is that all AI models have a similar level of comprehension when it comes to language.
One other thing to note at this point is that 90% of this text [The Common Crawl] is in English. Out of the remaining 10% non-English text 90% of this is religious texts - what this means is AI models work much better in English. If you need to create a CV and Cover Letter in your local tongue then your best bet is to do all the work in English and do a manual translation as the last step.
The difference to the available engines come from the data that has been trimmed from the model and the rulesets that have been applied to force the model into producing content in a specific method.
These things considered, because they were designed specifically for the production of computer code, we will first remove the GitHub CoPilot, and the Phind LLMs from our list of resources.
Let's talk about OpenAI's ChatGPT, Microsoft CoPilot, Anthropic's Claude, Meta's LLaMa, Gemini from Google (we consider the offering from X to be a humorous attempt by Musk to capture some free publicity and not a real contender).
We did our testing using the free versions of these engines, ensuring that our methods become available to everybody.
ChatGPT is limited to version 3 by OpenAI, because Version 4 is available through their subscription program only - and v4 is actually much better than v3 - however, Microsoft CoPilot uses the OpenAI GPT-4 Turbo as its engine - so getting free access to GPT-4 is as simple as using Microsoft CoPilot.
Gemini has a benefit of being able to access websites during your Q&A session - which might be useful in some instances, but as we are going into this with our CV and Job Description already prepared is not a function we necessarily need.
In testing, LLaMa from Meta and Google Gemini produced results that were not as polished as CoPilot.
However, we had one clear winner - Anthropic's LLM, Claude.
Claude produced consistent, high quality results across a range of CVs and Job Descriptions.
You can find Claude here: https://claude.ai