AI cover letter generators have exploded in the past two years. The promise is real: instead of staring at a blank page for 40 minutes, you get a first draft in 30 seconds. But the quality gap between tools is enormous.
Most produce the same letter with different names swapped in. A few actually work. Here is what separates them.
Why Most AI Cover Letters Fail
The problem is not the AI — it is what the AI is given to work with. Tools that only ask for your job title and the company name will always produce generic output. There is not enough signal to write anything specific.
A cover letter that says “I am excited to bring my skills and experience to your team” was almost certainly written by an AI with too little context. Hiring managers see hundreds of these. They get ignored.
What a Good AI Tool Actually Needs
To generate a letter worth sending, an AI tool needs three things:
- The full job description. Not just the title — the actual requirements, responsibilities, and keywords the company used.
- Your specific background. Relevant experience, measurable achievements, tools and skills that match this role.
- Context about the match. What are your strongest fits? What gaps might the recruiter notice? What angle should the letter lead with?
Without all three, the AI is guessing. The more context you give it, the better the output.
Generic AI vs. Purpose-Built Tools
You can paste a job description into ChatGPT and ask it to write a cover letter. It works — but you get a general-purpose output from a general-purpose tool. It does not analyse how well you match the role. It does not tell you what gaps a recruiter might notice. It does not suggest the specific angle your letter should lead with.
Purpose-built tools are different because they are designed around the actual job application workflow. The best ones do the analysis first — then write the letter based on that analysis.
What to Look For
- Job fit analysis. The tool should tell you how well you match the role before writing the letter, so the letter addresses the right things.
- ATS optimisation. The output should include keywords from the job description naturally, not stuffed in.
- Tone control. Different roles need different tones. A startup engineering role and a corporate legal position need different letters.
- Editability. The best tools give you a strong first draft you can refine, not a finished product you are expected to accept as-is.
How CoverDraft Approaches It
CoverDraft starts with a job fit analysis: it reads the job description against your background and produces a match score, your key strengths, your gaps, and — on Pro — the specific angle your letter should lead with.
The letter is then written around that analysis. If you are a strong match on technical skills but light on management experience, the letter leads with the technical depth and addresses the management gap briefly rather than ignoring it.
The result is a letter that sounds like it was written by someone who actually read the job post — because the analysis behind it actually did.
Tips for Getting the Best Output
If you are new to using AI for cover letters, start with understanding the right workflow — the tool is only as good as what you give it.
- Paste the full JD, not just the title. The more specific the requirements, the more specific the letter.
- Include metrics in your background. “Grew revenue by 40%” will show up in the letter. “Worked on sales” will not.
- Always read and edit the output. AI drafts are starting points. One or two personal touches — a specific company mention, a relevant anecdote — make the final letter noticeably better.
- Check the job fit score. If you are a 55% match, the letter needs to work harder on your transferable strengths. If you are 85%, lean into the specifics.