Context-Aware AI Drafting in Browser Extensions

Autofilling basic fields like name, email, and phone numbers is a solved technical problem. But how do you handle complex, open-ended career application prompts (e.g. "Describe your experience managing cloud transitions" or "Why are you interested in our team?") without resorting to generic, robotic paragraphs? In this article, we'll outline the technical challenges of client-side DOM scraping and show how Jobies compiles contextual AI drafts dynamically.

The Selector Chaos of Modern Job Boards

Automating text generation on job application boards is difficult because of markup inconsistency. Every Applicant Tracking System (ATS)—including Greenhouse, Lever, Workday, and custom systems—uses completely different class naming schemas and nested iframe structural trees. A textarea field on Greenhouse might look like a simple input, while on Workday it is nested three layers deep inside a shadow DOM root.

To overcome this, Jobies implements a localized parsing pipeline. Instead of relying on hardcoded element IDs, the extension analyzes label text, nearby headings, and placeholder tags using custom CSS selector scoring rules. This yields a semantic map identifying exactly what details each form section expects.

1. Client-Side DOM Scraping & Context Isolation

When the user triggers the extension, Jobies scrapes the active tab's job description. We look for specific headers (e.g., "About the Role", "Requirements", "What You'll Do") and extract the text content. To keep token sizes lean and stay within LLM context limits, our local parser automatically filters out boilerplate marketing, cookie disclaimers, and legal clauses. We compile the core description into a simplified JSON metadata object.

2. Constructing the Prompt Pipeline

The compiled job description is combined with the candidate's locally stored resume profile. Instead of passing an unformatted, massive resume string, the Jobies profile builder structures experience items by dates, roles, and concrete metric results. This enables our prompt templates to instruct the model to align candidate achievements with the parsed job requirements.

The consolidation process involves three key steps:

• Isolating Job Requirements: We identify the core technical skills, leadership values, and daily tasks listed in the job description, ranking them by frequency and emphasis.
• Selecting Relevant Experience: The local parser evaluates the candidate's profile to extract accomplishments that match the ranked job requirements, prioritizing metric-driven statements (e.g. "reduced load time by 40%" over "worked on web speed").
• Synthesizing Context: We construct a structured payload combining the filtered job requirements, matched achievements, and the specific application question to feed into the language model.

Managing LLM Token Budgets and Context Windows

Sending a full, raw resume alongside a complete job description to a Large Language Model (LLM) is inefficient. It increases token consumption, raising API costs, and can degrade response quality. When context windows are overloaded with irrelevant details, models are more likely to generate generic or unfocused content.

To solve this, Jobies performs client-side token optimization. Our local parser analyzes the text to identify and remove filler words, repetitive phrases, and generic formatting. By distilling the input data to its essential points before dispatching the request, we ensure the model receives high-signal context. This improves the quality of the generated drafts and ensures the responses remain focused and relevant.

3. Secure Execution and User Review

This customized prompt is securely dispatched to our generative API. Once the model returns a draft, Jobies inserts the text directly into the target textarea and dispatches input change events so the form registers the value. Because privacy is our priority, candidate records are never persisted on our servers. Users can instantly read, modify, and polish the AI-generated answers directly in their browser before submission.

This interactive review stage is essential. Rather than fully automating the submission, we believe keeping the candidate in the loop ensures the final application is authentic, accurate, and reflects their true professional voice.

Summary

Jobies Pro takes the hassle out of custom applications. By combining scraped job context and candidate profiles securely, we draft quality, tailored answers in seconds. Try the Jobies browser extension today to automate your career search pipeline.