Job Application Processing
Upload a CV to auto-extract details via AI and pre-fill the application form for quick review.
What it does
Upload your CV to instantly extract key details and auto-fill the job application form for your quick review. The system validates your resume using AI, pulls relevant information based on the job post into Airtable, and generates a pre-filled draft ready for you to submit.
Step by step
- Configure the n8n form trigger to accept multipart/form-data for CV uploads.
- Set up the Airtable base with fields matching the extracted resume keys such as name, email, and skills.
- Define the second n8n form schema to pre-fill data from the previous node's output.
Where the LLM does the work
- Craft a system prompt that instructs the LLM to extract specific fields while ignoring irrelevant resume sections.
- Design the extraction logic to handle varied resume formats by asking for normalized JSON output.
- Implement error handling in the LLM node to retry if the response is not valid JSON.
Watch out for
Large PDF files can exceed token limits so convert documents to plain text before sending them to the model.
Sensitive personal data is exposed in logs so ensure you mask PII fields in your n8n workflow settings.
Tools that fit
n8n Forms Service Collecting CV uploads and application data
Text Classifier API Validating PDF files as valid CVs
OpenAI Chat Model LLM Extracting relevant information from CVs
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Job Application Processing: Upload a CV to auto-extract details via AI and pre-fill the application form for quick review. ## Specification - What it does: Upload your CV to instantly extract key details and auto-fill the job application form for your quick review. The system validates your resume using AI, pulls relevant information based on the job post into Airtable, and generates a pre-filled draft ready for you to submit. - Trigger: Triggered by an event (Manual · on new form submission) - Autonomy: You stay in control - Expected setup effort: about an afternoon - Tools/services involved: - n8n Forms: Collecting CV uploads and application data - Text Classifier: Validating PDF files as valid CVs - OpenAI Chat Model: Extracting relevant information from CVs - Airtable: Storing applicant data and PDF attachments ## Known pitfalls, handle each one explicitly in your implementation 1. Large PDF files can exceed token limits so convert documents to plain text before sending them to the model. 2. Sensitive personal data is exposed in logs so ensure you mask PII fields in your n8n workflow settings. ## Reference implementation https://n8n.io/workflows/2579 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automated-job-application-processing/ via usecasesforagents.com
Frequently asked questions
Can I use a different LLM than OpenAI?
Yes. OpenAI is only the example LLM in this recipe. The same flow works with Anthropic, Cohere, DeepSeek and Google Gemini. Swap the LLM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for HR and recruiting teams who need a repeatable first pass, not a one-off.