Interview Trainer Agent

This project is an advanced AI-powered assistant designed to prepare users for job interviews. Leveraging the Llama 3 model on IBM's watsonx.ai platform, the agent provides a personalized and strategic preparation experience based on a user's specified job role and experience level.

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Key Features

• Personalized Prep Plans: Generates custom interview dossiers tailored to specific job roles and experience levels.
• RAG-Powered Insights: Uses a Retrieval-Augmented Generation (RAG) architecture to pull from a dedicated knowledge base of job descriptions, technical questions, and interview strategies.
• Strategic Guidance: Provides not just questions, but also key areas of focus, what questions the candidate should ask, and high-impact preparation tips.
• Cloud-Native Deployment: Deployed as a secure and scalable API endpoint on IBM Cloud, ready for integration into other applications.

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Architecture & Technology

• Platform: IBM Cloud
• Core Service: IBM watsonx.ai (AI Studio)
• Foundation Model: llama-3-70b-instruct
• Architecture: Retrieval-Augmented Generation (RAG)
• Knowledge Base: The agent is connected to a knowledge base containing documents on:
  • Real-world Job Descriptions (e.g., AI Prompt Engineer, MLOps Engineer)
  • Technical and Conceptual Interview Questions
  • Behavioral and Situational Questions
  • Proven Answering Strategies (like the STAR method)
• Deployment: IBM Cloud Deployment Space

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How It Works

The agent follows a sophisticated RAG workflow to generate its response:

1. User Prompt: The agent receives a prompt from the user, such as "Prepare me for a Full Stack Developer role."
2. Knowledge Retrieval: It searches its vector index (knowledge base) to find the most relevant documents related to the user's request.
3. Prompt Augmentation: It synthesizes the user's request with the retrieved information into a detailed, structured prompt for the foundation model. This prompt is guided by the advanced "Agent Instructions" provided during setup.
4. Response Generation: The llama-3-70b-instruct model processes the augmented prompt and generates the final, structured "Interview Dossier" for the user.

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How to Use the Deployed Agent

The deployed agent is accessible via an API endpoint. To interact with it, you would send a POST request with your API key and input.

Sample Python Code:

import requests

# NOTE: Replace with your actual API key and endpoint URL from the deployment
IAM_API_KEY = "YOUR_IBM_CLOUD_API_KEY"
ENDPOINT_URL = "YOUR_DEPLOYMENT_ENDPOINT_URL"

# Get an IAM token
token_response = requests.post(
    "[https://iam.cloud.ibm.com/identity/token](https://iam.cloud.ibm.com/identity/token)",
    headers={"Content-Type": "application/x-www-form-urlencoded"},
    data={"grant_type": "urn:ibm:params:oauth:grant-type:apikey", "apikey": IAM_API_KEY}
)
iam_token = token_response.json()["access_token"]

# Define the payload for the agent
payload = {
    "input": "Prepare me for a Senior Data Analyst interview"
}

# Send the request to the agent
response = requests.post(
    ENDPOINT_URL,
    headers={
        "Authorization": f"Bearer {iam_token}",
        "Content-Type": "application/json"
    },
    json=payload
)

if response.status_code == 200:
    print(response.json())
else:
    print(f"Error: {response.status_code}, {response.text}")

Deployment Screenshots

Agent Deployed on IBM Cloud

Agent Configuration & Custom Instructions

Agent in Action (Preview)