From Resume to Cowl Letter Utilizing AI and LLM, with Python and Streamlit

DISCLAIMER: The thought of doing Cowl Letter and even Resume with AI doesn’t clearly begin with me. Lots of people have completed this earlier than (very efficiently) and have constructed web sites and even firms from the concept. That is only a tutorial on find out how to construct your individual Cowl Letter AI Generator App utilizing Python and some strains of code. All of the code you’ll say on this weblog submit will be present in my public Github folder. Take pleasure in. 🙂 

Pep Guardiola is a (very profitable) Manchester Metropolis soccer coach. Throughout Barcelona’s Leo Messi years, he invented a manner of taking part in soccer generally known as “Tiki-Taka”. Because of this as quickly as you obtain the ball, you move the ball, instantly, with out even controlling it. You’ll be able to move the ball 30–40 occasions earlier than scoring a objective.

Greater than a decade later, we are able to see how the way in which of taking part in soccer made Guardiola and his Barcelona well-known is gone. Should you have a look at a Manchester Metropolis match, they take the ball and instantly search for the striker or the winger. You solely want just a few, vertical passes, instantly searching for the chance. It’s extra predictable, however you do it so many occasions that you’ll finally discover the area to hit the goal.

I feel that the job market has one way or the other gone in the identical route. 

Earlier than you had the chance to go to the corporate, hand in your resume, discuss to them, be round them, schedule an interview, and actively discuss to individuals. You’d spend weeks making ready for that journey, sprucing your resume, and reviewing questions and solutions. 

For a lot of, this old school technique nonetheless works, and I imagine it. You probably have an excellent networking alternative, or the fitting time and place, the handing the resume factor works very effectively. We love the human connection, and it is extremely efficient to really know somebody. 

It is very important contemplate that there’s a complete different method as effectively. Corporations like LinkedIn, Certainly, and even on the whole the web fully modified the sport. You’ll be able to ship so many resumes to so many firms and discover a job out of statistics. AI is altering this sport a bit bit additional. There are numerous AI instruments to tailor your resume for the particular firm, make your resume extra spectacular, or construct the job particular cowl letter. There are certainly many firms that promote this sort of providers to individuals which can be searching for jobs.

Now, imagine me, I’ve acquired nothing in opposition to these firms, in any respect, however the AI that they’re utilizing it’s not likely “their AI”. What I imply by that’s that should you use ChatGPT, Gemini, or the tremendous new DeepSeek to do the precise process you’ll very probably not get a worse response than the (paid) software that you’re utilizing on their web site. You’re actually paying for the “commodity” of getting a backend API that does what we must do by way of ChatGPT. And that’s honest. 

Nonetheless, I need to present you that it’s certainly quite simple and low cost to make your individual “resume assistant” utilizing Giant Language Fashions. Particularly, I need to give attention to cowl letters. You give me your resume and the job description, and I provide you with your cowl letter you’ll be able to copy and paste to LinkedIn, Certainly, or your e mail.

In a single picture, it can appear to be this:

Now, Giant Language Fashions (LLMs) are particular AI fashions that produce textual content. Extra particularly, they’re HUGE Machine Studying fashions (even the small ones are very huge). 

Because of this constructing your individual LLM or coaching one from scratch could be very, very costly. We gained’t do something like that. We are going to use a wonderfully working LLM and we are going to neatly instruct it to carry out our process. Extra particularly, we are going to try this in Python and utilizing some APIs. Formally, it’s a paid API. Nonetheless, since I began the entire challenge (with all of the trial and error course of) I spent lower than 30 cents. You’ll probably spend 4 or 5 cents on it.  

Moreover, we are going to make a working internet app that can permit you to have your cowl letter in just a few clicks. It is going to be an effort of a pair hundred strains of code (with areas 🙂).

To encourage you, listed below are screenshots of the ultimate app:

Fairly cool proper? It took me lower than 5 hours to construct the entire thing from scratch. Consider me: it’s that easy. On this weblog submit, we are going to describe, so as:

1. The LLM API Technique. This half will assist the reader perceive what LLM Brokers we’re utilizing and the way we’re connecting them.
2. The LLM Object. That is the implementation of the LLM API technique above utilizing Python. 
3. The Internet App and outcomes. The LLM Object is then transferred into an internet app utilizing Streamlit. I’ll present you find out how to entry it and a few outcomes. 

I’ll attempt to be as particular as attainable so that you’ve all the pieces you might want to make it your self, but when these things will get too technical, be happy to skip to half 3 and simply benefit from the sundown 🙃.

Let’s get began!

1. LLM API Technique

That is the Machine Studying System Design a part of this challenge, which I stored extraordinarily gentle, as a result of I wished to maximise the readability of the entire method (and since it truthfully didn’t should be extra sophisticated than that).

We are going to use two APIs:

1. A Doc Parser LLM API will learn the Resume and extract all of the significant data. This data shall be put in a .json file in order that, in manufacturing, we can have the resume already processed and saved someplace in our reminiscence.
2. A canopy letter LLM API. This API will learn the parsed resume (the output of the earlier API) and the job description and it’ll output the Cowl Letter.

Two details:

1. What’s the finest LLM for this process? For textual content extraction and summarization, LLama or Gemma are recognized to be a fairly low cost and environment friendly LLM. As we’re going to use LLama for the summarization process, to be able to hold consistency, we are able to undertake it for the opposite API as effectively. If you wish to use one other mannequin, be happy to take action.
2. How will we join the APIs? There are a number of how you are able to do that. I made a decision to provide it a attempt to Llama API. The documentation shouldn’t be precisely in depth, nevertheless it works effectively and it permits you to play with many fashions. You will have to log in, purchase some credit score ($1 is greater than ample for this process), and save your API key. Be at liberty to change to a different answer (like Hugging Face or Langchain) should you really feel prefer it.

Okay, now that we all know what to do, we simply want to really implement it in Python. 

2. LLM Object

The very first thing that we’d like is the precise LLM prompts. Within the API, the prompts are often handed utilizing a dictionary. As they are often fairly lengthy, and their construction is all the time related, it is sensible to retailer them in .json recordsdata. We are going to learn the JSON recordsdata and use them as inputs for the API name. 

2.1 LLM Prompts

On this .json file, you should have the mannequin (you’ll be able to name no matter mannequin you want) and the content material which is the instruction for the LLM. In fact, the content material key has a static half, which is the “instruction” and a “dynamic” half, which is the particular enter of the API name. For instance: that is the .json file for the primary API, I known as it resume_parser_api.json:

As you’ll be able to see from the “content material” there may be the static name:

“You’re a resume parser. You’ll extract data from this resume and put them in a .json file. The keys of your dictionary shall be first_name, last_name, location, work_experience, school_experience, expertise. In deciding on the data, hold observe of essentially the most insightful.”

The keys I need to extract out of my “.json” recordsdata are:

[first_name, last_name, location, work_experience, school_experience, skills]

Be at liberty so as to add something extra data that you simply need to be “extracted” out of your resume, however keep in mind that that is stuff that ought to matter solely to your cowl letter. The precise resume shall be added after this textual content to kind the total name/instruction. Extra on that later.

The order instruction is the cover_letter_api.json:

Now the instruction is that this one:

“You’re an professional in job searching and a canopy letter author. Given a resume json file, the job description, and the date, write a canopy letter for this candidate. Be persuasive {and professional}. Resume JSON: {resume_json} ; Job Description: {job_description}, Date: {date}”

As you’ll be able to see, there are three placeholders: “Resume_json”, “job_description” and “date”. As earlier than, these placeholders will then get replaced with the right data to kind the total immediate. 

2.2 constants.py

I made a really small constants.py file with the trail of the 2 .json immediate recordsdata and the API that you need to generate from LLamaApi (or actually no matter API you’re utilizing). Modify this if you wish to run the file regionally. 

2.3 file_loader.py

This file is a set of “loaders” to your resume. Boring stuff however essential. 

2.4 cover_letter.py

The entire implementation of the LLM Technique will be discovered on this object that I known as CoverLetterAI. There it’s:

I spent fairly a while attempting to make all the pieces modular and straightforward to learn. I additionally made numerous feedback to all of the capabilities so you’ll be able to see precisely what does what. How will we use this beast?

So the entire code runs in 5 easy strains. Like this:

from cover_letter import CoverLetterAI
cover_letter_AI = CoverLetterAI()
cover_letter_AI.read_candidate_data('path_to_your_resume_file')
cover_letter_AI.profile_candidate()
cover_letter_AI.add_job_description('Insert job description')
cover_letter_AI.write_cover_letter()

So so as:

1. You name the CoverLetterAI object. It is going to be the star of the present
2. You give me the trail to your resume. It may be PDF or Phrase and I learn your data and retailer them in a variable.
3. You name profile_candidate(), and I run my first LLM. This course of the candidate phrase data and creates the .json file we are going to use for the second LLM 
4. You give me the job_description and also you add it to the system. Saved.
5. You name write_cover_letter() and I run my second LLM that generates, given the job description and the resume .json file, the duvet letter

3. Internet App and Outcomes

So that’s actually it. You noticed all of the technical particulars of this weblog submit within the earlier paragraphs.

Simply to be additional fancy and present you that it really works, I additionally made it an internet app, the place you’ll be able to simply add your resume, add your job description and click on generate cowl letter. That is the link and that is the code.

Now, the cowl letters which can be generated are scary good.

This can be a random one:

February 1, 2025

Hiring Supervisor,
[Company I am intentionally blurring]

I’m thrilled to use for the Distinguished AI Engineer place at [Company I am intentionally blurring], the place I can leverage my ardour for constructing accountable and scalable AI methods to revolutionize the banking trade. As a seasoned machine studying engineer and researcher with a powerful background in physics and engineering, I’m assured that my expertise and expertise align with the necessities of this position.

With a Ph.D. in Aerospace Engineering and Engineering Mechanics from the College of Cincinnati and a Grasp’s diploma in Physics of Advanced Methods and Large Knowledge from the College of Rome Tor Vergata, I possess a singular mix of theoretical and sensible information. My expertise in growing and deploying AI fashions, designing and implementing machine studying algorithms, and dealing with massive datasets has geared up me with the abilities to drive innovation in AI engineering.

As a Analysis and Educating Assistant on the College of Cincinnati, I utilized surrogate fashions to detect and classify cracks in pipes, reaching a 14% enchancment in harm detection experiments. I additionally developed surrogate fashions utilizing deep studying algorithms to speed up Finite Factor Strategies (FEM) simulations, leading to a 1M-fold discount in computational time. My expertise in instructing and creating programs in sign processing and picture processing for teenagers interested by AI has honed my capability to speak advanced ideas successfully.

In my earlier roles as a Machine Studying Engineer at Gen 9, Inc., Apex Microdevices, and Accenture, I’ve efficiently designed, developed, and deployed AI-powered options, together with configuring mmWave radar and Jetson units for information assortment, implementing state-of-the-art level cloud algorithms, and main the FastMRI challenge to speed up MRI scan occasions. My experience in programming languages comparable to Python, TensorFlow, PyTorch, and MATLAB, in addition to my expertise with cloud platforms like AWS, Docker, and Kubernetes, has enabled me to develop and deploy scalable AI options.

I’m significantly drawn to [Company I am intentionally blurring] dedication to creating accountable and dependable AI methods that prioritize buyer expertise and ease. My ardour for staying abreast of the most recent AI analysis and my capability to judiciously apply novel methods in manufacturing align with the corporate’s imaginative and prescient. I’m excited in regards to the alternative to work with a cross-functional crew of engineers, analysis scientists, and product managers to ship AI-powered merchandise that rework how [Company I am intentionally blurring] serves its prospects.

Along with my technical expertise and expertise, I possess wonderful communication and presentation expertise, which have been demonstrated by way of my technical writing expertise at In direction of Knowledge Science, the place I’ve written complete articles on machine studying and information science, reaching a broad viewers of 50k+ month-to-month viewers.

Thanks for contemplating my utility. I’m keen to debate how my expertise and expertise can contribute to the success of the [Company I am intentionally blurring] and [Company I am intentionally blurring]’s mission to convey humanity and ease to banking by way of AI. I’m assured that my ardour for AI, my technical experience, and my capability to work collaboratively will make me a helpful asset to your crew.

Sincerely,

Piero Paialunga

They appear identical to I’d write them for a particular job description. That being stated, in 2025, you might want to watch out as a result of hiring managers do know that you’re utilizing AI to put in writing them and the “laptop tone” is fairly simple to identify (e.g. phrases like “keen” are very ChatGPT-ish lol). For that reason, I’d wish to say to use these instruments correctly. Certain, you’ll be able to construct your “template” with them, however you should definitely add your private contact, in any other case your cowl letter shall be precisely like the opposite 1000’s of canopy letters that the opposite candidates are sending in. 

That is the code to construct the online app. 

4. Conclusions 

On this weblog article, we found find out how to use LLM to transform your resume and job description into a particular cowl letter. These are the factors we touched:

1. Using AI in job searching. Within the first chapter we mentioned how job searching is now fully revolutionized by AI. 
2. Giant Language Fashions thought. It is very important design the LLM APIs correctly. We did that within the second paragraph
3. LLM API implementation. We used Python to implement the LLM APIs organically and effectively
4. The Internet App. We used streamlit to construct a Internet App API to show the facility of this method.
5. Limits of this method. I feel that AI generated cowl letters are certainly superb. They’re on level, skilled and effectively crafted. Nonetheless, if everybody begins utilizing AI to construct cowl letters, all of them actually look the identical, or at the least all of them have the identical tone, which isn’t nice. One thing to consider. 

5. References and different good implementations

I really feel that’s simply honest to say numerous good those who have had this concept earlier than me and have made this public and accessible for anybody. That is only some of them I discovered on-line.

Cover Letter Craft by Balaji Kesavan is a Streamlit app that implements a really related thought of crafting the duvet letter utilizing AI. What we do totally different from that app is that we extract the resume instantly from the phrase or PDF, whereas his app requires copy-pasteing. That being stated, I feel the man is extremely gifted and really inventive and I like to recommend giving a glance to his portoflio.

Randy Pettus has a similar idea as effectively. The distinction between his method and the one proposed on this tutorial is that he’s very particular within the data, asking questions like “present hiring supervisor” and the temperature of the mannequin. It’s very fascinating (and good) which you could clearly see the way in which he’s pondering of Cowl Letters to information the AI to construct it the way in which he likes them. Extremely advisable.

Juan Esteban Cepeda does an excellent job in his app as effectively. You may also inform that he was engaged on making it greater than a easy streamlit add as a result of he added the hyperlink to his firm and a bunch of critiques by customers. Nice job and nice hustle. 🙂

6. About me!

Thanks once more to your time. It means rather a lot ❤

My identify is Piero Paialunga and I’m this man right here:

I’m a Ph.D. candidate on the College of Cincinnati Aerospace Engineering Division and a Machine Studying Engineer for Gen 9. I speak about AI, and Machine Studying in my weblog posts and on Linkedin. Should you favored the article and need to know extra about machine studying and observe my research you’ll be able to:

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If you wish to ask me questions or begin a collaboration, go away a message right here or on Linkedin:

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