Turning Transcript Into a Job-Winning Resumé
Nov 18, 2025Inside our AI Upgrade for Creative Professionals cohort, Timothy Kertanis built a working resumé, cover letter, and interview prep system by customizing the off-the-shelf AI. A 100-question self-interview with ChatGPT helped inform and produce multiple PDF artifacts that feed his job search and prep system: a movie-style career script, a professional summary, role-by-role situations, and the raw transcript.
Then, Timothy patched it all together into a Claude project that ingests a job description and generates a tailored resumé, cover letter aligning bullet points and prose to the requirements, and explains its edits with rationale and evidence. Layer in deep company research, targeted voice-memo follow-ups, and a LinkedIn outreach map for cold emails. This is a repeatable way to prepare, apply, and present his best in the job search.
A system you can run.
Problem, Named
Writing felt like a weakness, and bullet points weren't capturing how he thinks about decisions, strategy, or different ways of approaching work. Timothy wanted material that reflects the person behind the lines on the page.
His starting point was simple and direct: plug the résumé into ChatGPT and have it ask questions. The intent was to move past task lists and into perspective, so the story carried substance during applications and conversations.
Interview Workflow
Timothy ran a sustained back-and-forth with the model that operated as a 100-question interview. The conversation pushed beyond surface details into judgment, methods, and personal views, and that depth mattered because the transcript preserved details he doesn't usually write down.
Treating the exchange as raw capture let him keep everything: questions, responses, and follow-ups, so he could reference exact phrasing and reuse the strongest parts without guessing.
Artifacts Created
The interview turned into several PDFs he can pick up and deploy. One artifact is a movie-style script that showcases his career from the beginning through to the present. Another is a professional summary that functions as a profile. He also produced bullet points pulled from the interview answers and assembled role-by-role situations that show what happened in specific contexts.
The raw transcript remained intact as the reference layer, ensuring nothing important slipped between drafts.
Role Tailoring
Next he used Claude to tailor the package for specific roles. Each run included the job he wanted to apply for and his résumé, and the system generated a customized prompt every time. It drew connections between the job description and his past history. It also ingested the interview transcript and answers to tell a coherent story instead of repeating the same bullets, and it produced a unique high-quality cover letter each time as part of the output.
The loop turned static materials into role-aligned documents built from his own input.
Trust Safeguards
He noticed bits of hallucination and tried to reduce them with prompt constraints, but errors still appeared. His fix was procedural and focused on transparent outputs: require the system to list the changes it made, explain the rationale behind each change, and show how those parallels were drawn.
The setup also provided evidence-based citations for its choices, which gave him a clear view of why specific edits showed up and where they originated in his materials.
Closing Gaps
Another friction point was the gap between what he had done and what he wanted to convey. Timothy addressed this by asking additional, targeted questions to surface missing detail. When it helped the process, he answered by voice memo and sent those responses back into the workflow. That kept the loop moving and pushed the outputs toward a clearer presentation of his candidacy based on the information he already had.
Company Research
He layered in Claude deep research on each company and folded those findings into the same pipeline. The tools surfaced information he considered important for preparation, and feeding that context alongside his résumé and transcript meant the system reflected the role and environment he was pursuing. This step supported stronger applications and better readiness for discussions.
Network Outreach
The workflow also surfaced people on LinkedIn connected to the positions he targeted, creating direct paths for cold emails. Timothy called this effective in the current job market, where standing out matters, and added it to his process for outreach and follow-ups. Mapping contacts against specific roles gave him practical next moves tied to each application.
The Move: Interview Prep
Timothy has an interview next week and plans to lean on deep research to get ready. He'll feed role context into the same pipeline that produced his materials, reference the 100-question transcript for lived examples, and walk in with documents already aligned to the job description.
The goal is simple: use the tailored resumé, cover letter, and story beats generated from his own answers to keep the conversation grounded in evidence while the research layer keeps responses specific to the company in front of him.
The Upgrade Signal
Timothy's build shows the shape of modern career craft: use accessible AI to run a real interview with yourself, turn the answers into durable artifacts, and tailor each application with transparent edits, rationales, and evidence tags.
Add deep company research, targeted follow-ups by voice memo, and a living contact map pulled from LinkedIn, and you get a workflow that compresses prep time while expanding signal. No mythology, no magic tricks, just a disciplined loop that maps job descriptions to lived history and explains every move it makes so you can trust the output and refine it fast.
The Invitation Forward
If this resonates, come make your own application engine with us. At The AI Upgrade, we teach builders to translate raw experience into working systems, the kind that interview you, generate role-specific documents, document their changes, and plug into research and outreach without breaking stride.
You bring your story and the roles you're aiming for; we help you wire up the loop so every submission carries clarity, context, and proof.
When you're ready to move from scattered drafts to a repeatable pipeline, join us at The AI Upgrade and start shipping work that stands out.
Kris Krüg is co-founder of The Upgrade Academy, recovering photographer, and professional instigator of creative chaos. He's spent 20+ years watching technology either amplify or stifle human potential. He's chosen amplification.
Peter Bittner entrepreneur, AI product thinker, new media journalist, and UC Berkeley lecturer, is the methodical yin to Kris's creative yang, turning chaos into deployable systems. Together, they've empowered hundreds of professionals.
