From Resume to Cover Letter Using AI and LLM, with Python and Streamlit

DISCLAIMER: The concept of doing Cowl Letter and even Resume with AI doesn’t clearly begin with me. Lots of people have executed this earlier than (very efficiently) and have constructed web sites and even corporations from the concept. That is only a tutorial on methods 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 put up may be present in my public Github folder. Get pleasure from. 🙂 

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

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

I feel that the job market has by some means gone in the identical path. 

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

For a lot of, this old school technique nonetheless works, and I consider it. When you have a great networking alternative, or the fitting time and place, the handing the resume factor works very nicely. We love the human connection, and it is vitally efficient to really know somebody. 

You will need to think about that there’s a complete different strategy as nicely. Corporations like LinkedIn, Certainly, and even normally the web utterly modified the sport. You may ship so many resumes to so many corporations and discover a job out of statistics. AI is altering this sport a bit of bit additional. There are quite a lot of 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 corporations that promote this sort of providers to individuals which are on the lookout for jobs.

Now, consider me, I’ve acquired nothing towards these corporations, in any respect, however the AI that they’re utilizing it’s not likely “their AI”. What I imply by that’s that for those who use ChatGPT, Gemini, or the tremendous new DeepSeek to do the precise job you’ll very possible not get a worse response than the (paid) software that you’re utilizing on their web site. You might be actually paying for the “commodity” of getting a backend API that does what we must do by means of ChatGPT. And that’s truthful. 

Nonetheless, I need to present you that it’s certainly quite simple and low cost to make your individual “resume assistant” utilizing Massive Language Fashions. Particularly, I need to concentrate on 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’ll appear to be this:

Now, Massive 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 large). 

Because of this constructing your individual LLM or coaching one from scratch may 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 well instruct it to carry out our job. Extra particularly, we are going to do this in Python and utilizing some APIs. Formally, it’s a paid API. Nonetheless, since I began the entire undertaking (with all of the trial and error course of) I spent lower than 30 cents. You’ll possible spend 4 or 5 cents on it.  

Moreover, we are going to make a working net app that may permit you to have your cowl letter in a couple of clicks. Will probably be an effort of a pair hundred strains of code (with areas 🙂).

To inspire 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 put up, 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 Net App and outcomes. The LLM Object is then transferred into an internet app utilizing Streamlit. I’ll present you methods to entry it and a few outcomes. 

I’ll attempt to be as particular as doable so that you’ve got every thing it is advisable to make it your self, but when these items 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 undertaking, which I saved extraordinarily gentle, as a result of I needed to maximise the readability of the entire strategy (and since it actually didn’t have to 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 info. This info might be put in a .json file in order that, in manufacturing, we may 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 greatest LLM for this job? 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 job, with a view to preserve consistency, we are able to undertake it for the opposite API as nicely. If you wish to use one other mannequin, be happy to take action.
2. How can we join the APIs? There are a selection of how you are able to do that. I made a decision to offer it a attempt to Llama API. The documentation is just not precisely in depth, but it surely works nicely and it lets you play with many fashions. You have to to log in, purchase some credit score ($1 is greater than adequate for this job), and save your API key. Be at liberty to change to a different resolution (like Hugging Face or Langchain) for those who 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 want 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 comparable, 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. After all, 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 info from this resume and put them in a .json file. The keys of your dictionary might be first_name, last_name, location, work_experience, school_experience, expertise. In choosing the data, preserve 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 info that you just need to be “extracted” out of your resume, however do not forget that that is stuff that ought to matter solely to your cowl letter. The particular resume might be added after this textual content to type the complete name/instruction. Extra on that later.

The order instruction is the cover_letter_api.json:

Now the instruction is that this one:

“You might be an professional in job looking 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 proper info to type the complete 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 must 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 may be discovered on this object that I known as CoverLetterAI. There it’s:

I spent fairly a while attempting to make every thing modular and straightforward to learn. I additionally made quite a lot of feedback to all of the capabilities so you’ll be able to see precisely what does what. How can 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. Will probably 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 info and retailer them in a variable.
3. You name profile_candidate(), and I run my first LLM. This course of the candidate phrase information 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. Net App and Outcomes

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

Simply to be further 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 are generated are scary good.

It is 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 techniques to revolutionize the banking business. As a seasoned machine studying engineer and researcher with a robust 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 Complicated Techniques and Massive Knowledge from the College of Rome Tor Vergata, I possess a novel mix of theoretical and sensible data. My expertise in growing and deploying AI fashions, designing and implementing machine studying algorithms, and dealing with massive datasets has outfitted 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, attaining a 14% enchancment in injury detection experiments. I additionally developed surrogate fashions utilizing deep studying algorithms to speed up Finite Ingredient 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 considering AI has honed my skill 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 gadgets for information assortment, implementing state-of-the-art level cloud algorithms, and main the FastMRI undertaking 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 notably drawn to [Company I am intentionally blurring] dedication to creating accountable and dependable AI techniques that prioritize buyer expertise and ease. My ardour for staying abreast of the most recent AI analysis and my skill to judiciously apply novel strategies in manufacturing align with the corporate’s imaginative and prescient. I’m excited concerning the alternative to work with a cross-functional staff of engineers, analysis scientists, and product managers to ship AI-powered merchandise that remodel 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 means of my technical writing expertise at In the 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 software. 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 carry humanity and ease to banking by means of AI. I’m assured that my ardour for AI, my technical experience, and my skill to work collaboratively will make me a useful asset to your staff.

Sincerely,

Piero Paialunga

They appear similar to I’d write them for a selected job description. That being stated, in 2025, it is advisable to watch out as a result of hiring managers do know that you’re utilizing AI to write down them and the “pc 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 make sure to add your private contact, in any other case your cowl letter might be precisely like the opposite 1000’s of canopy letters that the opposite candidates are sending in. 

That is the code to construct the net app. 

4. Conclusions 

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

1. Using AI in job looking. Within the first chapter we mentioned how job looking is now utterly revolutionized by AI. 
2. Massive Language Fashions concept. You will need to 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 Net App. We used streamlit to construct a Net App API to show the ability of this strategy.
5. Limits of this strategy. I feel that AI generated cowl letters are certainly excellent. They’re on level, skilled and nicely crafted. Nonetheless, if everybody begins utilizing AI to construct cowl letters, all of them actually look the identical, or a minimum of 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 truthful to say quite a lot of good people 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 comparable concept of crafting the duvet letter utilizing AI. What we do completely different from that app is that we extract the resume immediately from the phrase or PDF, whereas his app requires copy-pasteing. That being stated, I feel the man is extremely proficient and really artistic and I like to recommend giving a glance to his portoflio.

Randy Pettus has a similar idea as nicely. The distinction between his strategy and the one proposed on this tutorial is that he’s very particular within the info, asking questions like “present hiring supervisor” and the temperature of the mannequin. It’s very attention-grabbing (and good) that you may clearly see the best way he’s pondering of Cowl Letters to information the AI to construct it the best way he likes them. Extremely really useful.

Juan Esteban Cepeda does an excellent job in his app as nicely. It’s also possible to 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 evaluations by customers. Nice job and nice hustle. 🙂

6. About me!

Thanks once more to your time. It means loads ❤

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. In the event you preferred 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, depart a message right here or on Linkedin:

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