Interview Questions

The AI agent takes over the routine part of interview preparation: reading the resume, matching it against the job description, and drafting a question list. The recruiter no longer has to start from scratch each time and receives a structured script that only needs to be reviewed and refined to fit the specific candidate's context.

Typical workflow:

1. The recruiter or hiring manager uploads the candidate's resume and job description to the system — via HRIS integration or directly to file storage.
2. The AI agent extracts key competencies from the role description and matches them against the experience listed in the resume.
3. The agent drafts a question list: behavioral, technical, situational — depending on the role level and interview stage.
4. Each question is accompanied by context from the resume — a specific project, skill, or experience gap worth clarifying in the conversation.
5. The finished script is saved in the HRIS or made available to the recruiter as a document before the meeting.
6. After the interview, the recruiter notes which questions worked best, and the feedback is factored into generating future scripts.

The result — each candidate receives an interview tailored to their experience, rather than a one-size-fits-all questionnaire. This matters especially for middle+ roles, where surface-level questions yield no signal about actual skills.

What automation does NOT do

• It does not make the hiring decision — that remains the task of the recruiter and hiring manager, relying on live discussion and expertise.
• It does not replace live dialogue: the generated script is a draft, not a rigid script that cannot be deviated from in conversation.
• It does not automatically assess soft skills or cultural fit based on answers — that requires human interpretation.

The approach falls under the 'content generation (drafts)' category: AI prepares the first version, a human validates it and sends it into production. This reduces the load on the creative part of the recruiter's work, but leaves final quality control in the hands of the team.

The technical implementation uses a no-code stack: a combination of an automation platform (for example, a workflow engine or Zapier), an LLM provider, and document storage. The recruiter writes no code — setup comes down to configuring nodes and prompts.

Data flow

Data follows a predictable route: resume source → LLM processing → script generation → delivery to the recruiter.

1. Trigger. A new resume appears in file storage (Google Drive, Dropbox, SharePoint) or in the HRIS candidates module. The automation platform catches the event.
2. Text extraction. The text layer is extracted from PDF or DOCX. For scans, an OCR step is added — this increases processing time.
3. Loading the job description. In parallel, the system retrieves the current job description from the HRIS or from a predefined document.
4. LLM call. The resume and job description are sent to the prompt together with a template: "compose N questions on competency X, tie them to specific items in the resume".
5. Structuring the response. The LLM returns the script in a structured format (Markdown or JSON), which is easier to parse and update.
6. Delivery. The finished script is saved back to the HRIS (attached to the candidate card) or sent to the recruiter as a document and a messenger notification.

Solution components

Implementation steps

1. Defining templates. The HR team defines what types of questions are needed for different roles (junior/middle/senior, engineering/sales/ops). This becomes a set of prompt templates.
2. Setting up integrations. Connecting the automation platform to the HRIS and file storage via ready-made connectors. At this step, it is verified that the system has read permissions for resumes and job descriptions.
3. Prompt engineering. Iterative prompt tuning: running 10–20 resumes through, comparing generated questions against what a recruiter would compose manually.
4. Output configuration. Choosing the script format — a Markdown document, a page in Notion, a card in HRIS. The team decides where it is more convenient to work with the result.
5. Pilot launch. Parallel operation: the recruiter prepares questions both manually and through the agent, then compares. Feedback is collected over 2–3 weeks of the pilot.
6. Rollout to the team. After the pilot — training the remaining recruiters (1–2 hours), adding a feedback loop to collect ratings after interviews.

Quality control

LLM-based content generation requires oversight: the model may miss context or produce a generic question. The minimum set of checks includes mandatory human review before the script goes into use, periodic quality audits on a sample, and an explicit ban on discriminatory phrasing (based on age, gender, or marital status). The prompt configuration embeds rules consistent with company policy and data protection legislation.

To launch automation, the team needs a minimal set of data and access. Most SMBs already have everything they need — an HRIS or cloud storage for documents is sufficient.

Data and access

• Up-to-date job descriptions listing key competencies and requirements.
• Candidate resumes in digital format (PDF or DOCX with a text layer).
• Read access to the HRIS or file storage where resumes are kept.
• Write-back permissions — to attach the script to the candidate's card.
• An account with an LLM provider with an active API key and a limit covering the expected volume of interviews.

Team readiness

• One person responsible for setup — a recruiter or HR ops, willing to spend 1–2 weeks configuring templates and prompts.
• Recruiters' agreement to work with a draft and bring it to a final script, rather than waiting for a "perfect" result from the model.
• An agreement with a lawyer or DPO that candidate resumes may be passed to an external LLM model (or an enterprise version with processing in the required jurisdiction is used).

Timeline by phase

Weekend complexity means launching the MVP version in 2–4 weeks:

1. Setting up integrations and job templates — 3–5 business days.
2. Prompt engineering on a sample of 10–20 resumes — 3–5 business days.
3. Pilot with one recruiter team and feedback collection — 1 week.
4. Rollout to the remaining recruiters — 1–2 days of training.

If no HRIS is in place and resumes are scattered across email, a step is added to consolidate data into a single storage — this extends the launch to 4–6 weeks.