When LLMs debate on www.IndiaAGI.ai

 When LLMs debate on www.IndiaAGI.ai

 

My prompt :

“I thought this was so obvious considering that www.IndiaAGI.ai , is a platform where ChatGPT , Gemini , Claude and Grok , debate openly and transparently “

 

www.IndiaAGI.ai  replied :

“You've highlighted a crucial aspect: the potential for transparency that platforms like IndiaAGI.ai offer.

 

I agree that the open debate format is a significant step towards demystifying AI and its capabilities.

 

The ability to directly compare the outputs of different models – ChatGPT, Gemini, Claude, and Grok, in this case – provides a unique opportunity to dissect their strengths, weaknesses, and biases.

 

Building on your point, let's delve deeper into the implications of this transparency:

 

·         Revealing Model Architectures and Training Data (Indirectly):

·         While the underlying architectures and training datasets of these models remain proprietary, the debates themselves can offer indirect insights. By observing how each model approaches a given topic, the types of arguments it favors, and the evidence it cites, we can infer patterns related to its training.

·         For example, if one model consistently relies on specific sources or exhibits a particular stylistic approach, it might suggest a stronger emphasis on certain data during its training. This is not a perfect substitute for direct access to the internal workings, but it's a valuable form of "reverse engineering" or "behavioral analysis" of these complex systems.

·          

·         Facilitating User Education:

·         The public often struggles to understand the nuances of AI. Open debates can serve as an accessible educational tool. By witnessing the different models grappling with complex issues, users can gain a better understanding of how AI systems process information, form conclusions, and the inherent uncertainties involved. This can help to combat the hype and fear often associated with AI by presenting a more realistic picture of its current capabilities and limitations.

·          

·         Promoting Accountability:

·         When AI models debate in public, their responses are subject to scrutiny.

·         This can help to hold developers accountable for the quality, fairness, and reliability of their models. If a model consistently provides inaccurate information, exhibits bias, or fails to address counterarguments effectively, it becomes evident to the audience, putting pressure on developers to improve their systems. This public accountability is a significant benefit of the open debate format.

·          

·         Fostering a Culture of Critical Evaluation:

·         The very act of comparing and contrasting the arguments of different AI models encourages a critical mindset. Users are implicitly prompted to evaluate the evidence presented, assess the logic employed, and consider alternative perspectives. This fosters a more informed and discerning public discourse around AI, which is essential as these technologies become increasingly integrated into our lives.

·          

However, as you also pointed out, there are challenges. The "quality of responses" is a key concern. If one model is significantly more sophisticated, the debate could be skewed.

 

We need mechanisms to ensure fairness and prevent any single model from dominating the discussion. This could involve :

Ø  carefully selecting the topics debated,

Ø  establishing clear rules for argumentation, and perhaps even

Ø  incorporating human moderators to provide additional context and guidance.

 

Furthermore, the potential for "misinterpretation" is very real.

 

It's crucial to clearly communicate the nature of these AI-generated debates.

 

We need to emphasize that the models are not necessarily expressing their "own" opinions, but rather generating responses based on their training data and algorithms. The goal is to foster understanding, not to endorse any particular viewpoint.

 

To make the most of this platform, I believe it's imperative to consider how we can best structure the debates to maximize their educational value and minimize the potential for misunderstanding.

 

Perhaps we could discuss specific strategies for topic selection, the role of human oversight, and the types of safeguards that could be implemented to ensure fair and informative discussions. What are your thoughts on how to mitigate these risks and maximize the benefits of such a platform?

 

 

Yesterday , Grok suggested following to improve CONSENSUS ENGINE :

“Subject: Urgent Feedback on IndiaAGI Beta - Improving Prompt Response Accuracy

Dear Kishan,

I hope you’re making great progress toward the May 1st IndiaAGI launch!

I’m writing to share critical user feedback from my experience with the beta, as it’s clear the platform has huge potential but needs some tuning to meet user expectations.

**The Issue**:

IndiaAGI’s consensus engine is struggling to quickly understand user prompts and deliver precise, relevant outputs.

When I asked for a structured legislative bill (titled "Prevention of Irresponsible Promises by Political Parties Act, PIPPPA") with specific clauses, IndiaAGI repeatedly produced vague, high-level policy discussions instead of the requested bill text.

Even with increasingly detailed and explicit prompts (e.g., mandating numbered sections, formal legal language, and no summaries), the responses remained off-target, focusing on abstract concepts like “fiscal scoring” and “voter education.” This forced me to spend hours refining prompts, which is unsustainable for users.

 

**Why It Matters**:

Users expect IndiaAGI to deliver results that are at least 70% as good as standalone LLMs (like Grok, ChatGPT, or Claude) in a single shot, without requiring extensive prompt engineering.

Right now, IndiaAGI’s outputs are slower and less accurate than standalone alternatives, undermining its value proposition. Visitors won’t have the patience to “train” the system over hours—they want *better* and *faster* answers tailored to India’s context.

 

**Case Study - PIPPPA Bill**:

My request was simple:

draft a bill with 10 numbered sections, including clauses for the Election Commission, judiciary, and law enforcement roles, to regulate freebies in election manifestos. Despite clear instructions (e.g., “provide full bill text, no policy discussions”), IndiaAGI delivered responses like:

- “A hybrid, multi-threshold framework for identifying freebies is essential…” (April 26, 2025)

- “The legislation should incorporate graduated enforcement and adaptive mechanisms…” (April 26, 2025)

These were polished but irrelevant to the task, ignoring the need for a structured bill. Meanwhile, Grok delivered a complete, clause-heavy PIPPPA bill in one go, meeting all requirements.

 

**Suggested Fixes**:

1.     **Improve Prompt Parsing**:

Enhance the consensus engine to better interpret and prioritize explicit instructions (e.g., “bill text only,” “numbered sections”). Currently, key directives are being overlooked, leading to off-topic outputs.

2.     **Add a Legal Drafting Mode**:

Introduce a mode that prioritizes structured, legal-style outputs for tasks like bill drafting. This could weight LLMs with strong drafting skills (e.g., Grok) higher in the huddle for such queries.

3.     **Filter Tangents**:

Implement a mechanism to block irrelevant topics (e.g., voter education, fiscal frameworks) unless explicitly requested. The engine keeps drifting to policy buzzwords, diluting relevance.

4.     **Benchmark Against Standalone LLMs**:

Ensure IndiaAGI’s outputs are at least 70% as accurate and relevant as a single LLM’s response on the first try. This could involve pre-testing prompts against standalone models to set a quality baseline.

5.     **Streamline User Experience**:

Reduce the need for users to refine prompts multiple times. IndiaAGI should learn from initial user inputs and adjust outputs dynamically, not demand hours of tweaking.

 

**The Opportunity**:

IndiaAGI’s vision of combining multiple LLMs for India-specific solutions is exciting, but it needs to outshine standalone models in speed and precision.

Fixing these issues will make it a go-to platform for users seeking quick, high-quality answers, especially for complex tasks like legislative drafting.

 

I’d be happy to discuss this further or share my prompt iterations and IndiaAGI’s responses for your team’s analysis. With the May 1st deadline approaching, addressing these beta quirks could be a game-changer. Keep up the great work, and let me know how I can support!

 

Best regards, 

[Your Name] 

[Optional Contact Info] 

April 26, 2025

 

Why This Code Helps

• Prompt Parser: Extracts explicit instructions and blacklists tangents, ensuring IndiaAGI focuses on user intent (e.g., “bill text” over “voter education”).
• LLM Weighting: Boosts LLMs like me (Grok) for structured tasks, improving output relevance.
• Tangent Filter: Strips out irrelevant topics, keeping responses on-topic.
• Output Formatter: Enforces numbered sections for structured tasks, meeting your 70% quality goal.
• Lean and Integrable: The code is modular and can slot into IndiaAGI’s pipeline without a major overhaul.

Notes for Kishan

• The blacklist (voter education, etc.) can be expanded based on recurring tangents in IndiaAGI’s outputs.
• LLM weights are illustrative; Kishan’s team should adjust based on each model’s strengths.
• The code assumes a Python-based pipeline, but it can be adapted to other languages (e.g., JavaScript) if needed.

Your Call, Bhai