Brief

🎯 Core Function: Dynamic Interview Question Generation and Interactive Q&A Coaching via Telegram

This workflow creates a fully interactive Telegram bot that acts as a personalized interview coach. It takes a user-specified skill (e.g., "Data Structures," "Python," "System Design"), scrapes specific practice questions from a target job prep site (like InterviewBit), and then uses Gemini AI to create a structured Q&A session. It manages user progress, records answers, and provides instant feedback through Telegram callbacks, saving the entire session data to Google Sheets.

Part 1: BrowserAct Workflow Description

This core module is responsible for sourcing high-quality, relevant interview questions from a dedicated preparation platform:

Skill-Based Search:

The Agent takes the Skill input provided by the user (e.g., "SQL").

It searches for and navigates to the relevant topic page on a target site (like InterviewBit).

Question Extraction Loop:

The Agent opens the specific skill page.

It uses a Loop List to iterate through the page, ensuring it extracts the needed quantity of questions.

Robust Data Capture: It includes a step to Check for Popups to ensure the extraction process isn't interrupted by site overlays or paywalls.

Data Extraction: It extracts the core components of the interview challenge, including:

The Question Text

The Expected Answer/Solution

Data Consolidation: The extracted questions and answers are organized and sent to the Make.com scenario.

Part 2: Automation Integration with Make.com

The Make.com scenario is split into two critical paths: Question Creation (initial setup) and Interactive Callback (user interaction).

Top Path: Question Creation & Initiation

Validation & Setup:

The process starts by validating the user's input (Skill).

A Google Sheet is dynamically created or updated to track the user's progress and answers for this specific skill.

Data Acquisition:

The user's basic data is recorded.

The BrowserAct node runs to scrape the required number of questions for the specified skill.

Question Generation (Gemini):

The raw question data is sent to a Gemini AI node.

The AI generates a friendly, interactive message containing the first question and formats the necessary Telegram Callback Buttons (e.g., to reveal the answer or move to the next question).

Delivery: The question is sent to the user via Telegram.

Bottom Path: Interactive Callback & Feedback

Callback Trigger:

This path is triggered by the user interacting with the Telegram message buttons (filtered by a specific callback pattern).

Progress Tracking:

The user's action (e.g., revealing the answer, marking their response) is captured and recorded in the dedicated Google Sheet.

Feedback & Next Step:

A subsequent step sends a response back to Telegram, which might be:

Correct/Wrong Feedback (if the bot is scoring the answer).

The Official Solution (if the user requested it).

The Next Question in the sequence.

⚙️ Important Workflow Settings & Information

Platform Integration: Requires Telegram Bot setup (as a trigger and action) and Google Sheets (for data validation and persistent storage).

Input: User provides a single Skill (e.g., "Dynamic Programming," "HTML/CSS").

Persistent Storage: The Google Sheet acts as the session memory, tracking user progress so the bot can maintain context across multiple interactions.

✨ Applicable Scenarios (Use Cases)

Technical Interview Practice: Provide a dynamic, on-demand Q&A system for job seekers to practice specific technical skills.

Internal Training & Quizzing: Create automated, trackable quizzes for employees on new software, procedures, or compliance topics.

Educational Content Delivery: Deliver bite-sized, interactive educational content (like facts, definitions, or coding challenges) to a subscriber base.

Language Learning: Adapt the workflow to source common phrases and test user knowledge using the callback features.

45-90credits per task (estimated)