Aggregate research answers from GPT 5.2, Claude Opus 4.6 and Gemini 3 Pro

This workflow implements a multi-model AI orchestration with the BEST models at now (ChatGPT 5.2, Claude Opus 4.6, Gemini 3 Pro) and response aggregation system designed to handle user chat inputs intelligently and reliably.

Key Advantages

1. ✅ Higher Answer Quality

By combining multiple top-tier AI models, the workflow reduces blind spots and single-model bias, resulting in more accurate and nuanced answers.

2.✅ Built-in Reliability and Redundancy

If one model underperforms or misunderstands the query, the others compensate, improving robustness and consistency.

3. ✅ Intelligent Query Handling

The search classification and optimization layer ensures that:

• research queries are handled with precision,
• casual conversation is not over-processed,
• model resources are used efficiently.

4. ✅ Balanced and Transparent Reasoning

Contradictions between models are not hidden. Instead, they are reconciled or clearly explained, increasing trust in the final output.

5. ✅ Scalability and Extensibility

The architecture makes it easy to:

• add new models,
• swap providers,
• experiment with different aggregation strategies,
  without redesigning the entire workflow.

6. ✅ Enterprise-Ready Design

This approach is well suited for:

• research assistants,
• decision-support tools,
• knowledge management systems,
• high-stakes professional use cases where answer quality matters more than speed alone.

How it Works

1. Input Processing: When a chat message is received, it's sent to a "Search Query Optimizer" that determines whether the input is a research query or general conversation. If it's a search query, it's optimized for better search results.

2. Multi-Model Query Execution: If the input is classified as a research query, the workflow simultaneously sends the optimized query to three different AI models:

   • ChatGPT 5.2 (OpenAI)
   • Claude Opus 4.6 (Anthropic)
   • Gemini 3 Pro (Google)

3. Response Aggregation: Each model's response is collected separately, then all three responses are sent to a "Multi-Response Aggregator" which synthesizes them into a single comprehensive answer.

4. Fallback Handling: If the input is not a research query, the workflow bypasses the multi-model execution and sends a default message asking the user to enter a research text.

Set up Steps

1. Model Configuration: Ensure you have valid API credentials set up for:

   • OpenAI (for ChatGPT 5.2)
   • Anthropic (for Claude Opus 4.6)
   • Google Gemini (for both query optimization and Gemini 3 Pro)

2. Connection Verification: Confirm all node connections are properly established in the workflow editor, particularly:

   • Chat trigger to Search Query Optimizer
   • Conditional branch routing based on query classification
   • Parallel connections to the three AI models
   • Response collection to the aggregator

3. Prompt Customization: Review and adjust the system prompts in:

   • Search Query Optimizer (for query classification rules)
   • Multi-Response Aggregator (for synthesis guidelines)
   • Each model's chain nodes (if specific formatting is required)

4. Testing: Activate the workflow and test with various inputs to verify:

   • Proper classification of research vs. non-research queries
   • Simultaneous execution of all three AI models
   • Correct aggregation of responses
   • Appropriate fallback message for non-research inputs

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