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