Stop Applying Blind: Use AI to Do Your Due Diligence Before You Hit Submit

Here’s something most job seekers never do: research a company before applying the same way a company researches you before hiring.

Think about that for a second. They’ve got your resume, your LinkedIn, maybe your GitHub. They’ll run you through multiple rounds of interviews. They’ll call your references. They’ll Google your name.

And you? You read a job description and hit Apply.

That’s insane.

The good news: you can fix this in about 20 minutes of setup and about 5 minutes per company after that. Here’s how.

The Setup

Pick your LLM of choice: Claude, ChatGPT, Gemini, whatever you’re comfortable with. The key is using a Project (or equivalent persistent context feature) so your resume stays loaded across every company you research.

Create a new Project
Upload your latest resume as a document in the Project instructions
Paste in the prompt below

That’s it. Every time you come across a job posting or company that looks interesting, you drop it into the chat and get a full due diligence report back in under five minutes. No digging through Crunchbase tabs, no hunting for Glassdoor reviews, no trying to reverse-engineer a salary range from three different job boards. It’s all in one shot.

The Prompt

Paste this into your Project instructions:

You have my resume (attached). When I provide a company name or job posting, 
research it thoroughly and return a structured report covering the following. 
If information is unavailable or unverifiable, say so explicitly. Do not 
estimate or fabricate.

**Company Health**
- Funding history: total raised, rounds, dates, lead investors
- Current financial state and estimated runway (public financials or last 
  known funding date)
- Stage and risk profile (seed / Series A-C / late-stage / public)

**Market**
- TAM: cite the source and year for any figure used
- Primary competitors and how this company differentiates from them
- Core differentiation: what do they do that competitors don't, or do 
  materially better?

**Role Details**
- Who this position likely reports to: check the posting, LinkedIn org 
  charts, or recent interviews
- Whether this role has been posted before or the team has had notable 
  turnover (backfill signal)
- Salary range extrapolated from similar postings, Levels.fyi, Glassdoor, 
  and comparable roles. State your confidence level.

**Tech Stack**
- Inferred from engineering job postings, job descriptions, engineering 
  blog posts, or StackShare

**Employee Sentiment**
- Glassdoor: overall rating, common praise and complaints, CEO approval
- Reddit: relevant threads from r/cscareerquestions, r/ExperiencedDevs, 
  or company-specific subs
- Blind or Levels.fyi reviews if available
- Note any patterns: culture, management quality, work/life balance, 
  compensation fairness

**Red Flags**
- Recent layoffs, leadership churn, funding gaps, or long time-to-fill 
  on this role
- Litigation, regulatory issues, or press coverage of internal problems
- Glassdoor or Reddit sentiment that is notably negative or contradicts 
  company messaging

**Fit Assessment**
- Based on my resume, assess how well I match this role
- Highlight gaps or risks in my candidacy
- Note any particular strengths that align with what the company appears 
  to need

Cite URLs for every claim where a source exists. Be specific and concise.

What You Actually Get Back

A good LLM running this prompt will surface things you’d never think to look for:

That “hot startup” raised its last round in 2021 and hasn’t announced anything since. That’s a runway problem.
The role has been reposted three times in 18 months. That’s a management problem.
Glassdoor is a 3.2 with “leadership changes” mentioned in every recent review. That’s a run problem.
The salary band for this title at this company stage in this city is $40k below what you’re targeting. That’s a waste-of-time problem.

You find all of this before you spend four hours doing a take-home assignment.

What It Won’t Get Right (Every Time)

The LLM will tell you when it’s uncertain, or it should, if you’ve included “do not estimate or fabricate” in the prompt. A few areas that are genuinely hard to verify:

Backfill status. Whether a role is a backfill is almost never public information. The prompt looks for signals (repeated postings, suspiciously fast headcount growth) but it’s inference, not fact.

Internal org structure. Who a role reports to is often missing from the posting. The LLM will dig for it on LinkedIn or in press coverage, but treat this as a starting point, not a definitive answer.

Recent employee sentiment. Glassdoor reviews skew toward people with strong feelings: the ones who loved it or hated it. The LLM can surface patterns but can’t tell you if a 3.8 rating reflects an improving company or a declining one.

Use the report as a first filter, not the final word. Follow up on anything that’s ambiguous.

Why This Changes How You Apply

The typical job search drifts into a numbers game: blast applications, see what sticks, then research the company when they email you back. That’s backwards.

You’re wasting time on companies that would have been obvious nos after five minutes of research. And you’re going into early conversations underprepared, without the context that would let you ask smart questions and signal real interest.

ProTip

This approach pairs especially well with Fitcheck, a tool that automatically ingests job alert emails from LinkedIn, Indeed, and others, scores each role against your criteria, and surfaces only the ones worth your time. Once Fitcheck has filtered your shortlist, the due diligence prompt becomes the second step in a tight loop: good fit gets flagged, you run the report, you decide in minutes whether it’s worth pursuing. If you want a cleaner mailbox setup first, this Gmail forwarding walkthrough shows how to route only approved job-alert sources into a dedicated inbox. The buckshot approach to applying gets replaced by something much more deliberate.

The due diligence report does the rest. You know the company’s funding story before the recruiter screen. You know the competitive landscape before the hiring manager call. You know the employee sentiment before you decide whether to prep hard or pass entirely.

You show up informed. That’s not just more efficient. It’s actually impressive, because almost nobody does it. And in a market shaped by big-tech layoff waves, that extra discipline matters even more.

One More Thing

The fit assessment section is underrated. Most people evaluate job fit by gut feel: does this job description sound like me? The LLM does something more useful. It maps your actual experience against the actual requirements and tells you where you’re strong, where you’re thin, and what you’d need to address in an interview.

That alone is worth the setup time.

Set up the project once. Use it every time you see a posting worth considering. Spend your prep time on the companies that actually deserve it.