AI-Powered Publisher Chatbot with Ad Monetization
Develop a multi-agent system using CrewAI to power an interactive AI chatbot for publisher websites. This system will dynamically respond to user queries based on the publisher's content, suggest related articles, and intelligently integrate contextual ad placements, aiming for a revenue-sharing model. It will use Gemini 2.5 Pro for deep content understanding and generation, alongside Claude Opus 4.1 for nuanced ad content and user interaction, showcasing advanced RAG and hybrid reasoning techniques.
What you are building
Build a CrewAI-orchestrated multi-agent system combining RAG, content generation, and ad integration using Gemini 2.5 Pro and Claude Opus 4.1.
How work is evaluated
The solution will be evaluated on the functionality of the multi-agent system, the accuracy and relevance of generated content and ads, and the overall architecture's adherence to modern agentic principles.
Datasets
Public datasets, plus the private datasets you have access to.
Learning goals
Integrate advanced RAG techniques for real-time content retrieval and synthesis.
Design and implement a multi-agent system with CrewAI for complex web applications.
Develop agents capable of contextual ad placement and revenue optimization.
Orchestrate agent interactions for a seamless user experience.
Utilize hybrid reasoning (instant/deep) with Gemini 2.5 Pro for dynamic responses.
Resources
Sample data for 1 tasks
Challenge setup
The solution will be evaluated on the functionality of the multi-agent system, the accuracy and relevance of generated content and ads, and the overall architecture's adherence to modern agentic principles.
Challenge input
{ "user_query": "string", "publisher_content": "string[]", "mock_ad_database": "object[]" }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ "chatbot_response": "string", "suggested_ad": "object | null" }
- Checks if CrewAI agents are properly instantiated, communicate effectively, and delegate tasks according to...
- Verifies that chatbot responses are factually accurate and directly grounded in the provided `publisher_con...
- Ensures the `suggested_ad` is highly relevant to the `user_query` and the `chatbot_response`'s context, bas...
- ResponseCoherence target: 0.9
- Python execution harness
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Checks if CrewAI agents are properly instantiated, communicate effectively, and delegate tasks according to the defined workflow.
- Verifies that chatbot responses are factually accurate and directly grounded in the provided `publisher_content`, minimizing hallucination.
- Ensures the `suggested_ad` is highly relevant to the `user_query` and the `chatbot_response`'s context, based on keywords or semantic similarity.
Metrics
- ResponseCoherence · Target 0.9
- AdRelevanceScore · Target 0.8
- ContentUtilizationRate · Target 0.75
Workflow tasks
- Task 1
Input: { "user_query": "string", "publisher_content": "string[]", "mock_ad_database": "object[]" }
Output: { "chatbot_response": "string", "suggested_ad": "object | null" }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.