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New AI system PAWNI optimizes prompt formulation for LLMs

By PulseAugur Editorial · [3 sources] ·

Researchers have developed PAWNI (Prompt Architecture Wizard using Neural Intelligence), a multi-agent conversational system designed to improve prompt formulation for large language models (LLMs). PAWNI utilizes eight agents to guide users through a dialogue, transforming unstructured queries into structured prompts and optimizing the question itself rather than the model's response. An exploratory study with four participants showed that PAWNI significantly increased the structural completeness of prompts, improved LLM output quality, and reduced user workload, enabling satisfactory results in a single turn. AI

IMPACT This system could significantly improve the efficiency and effectiveness of human interaction with LLMs, reducing wasted turns and improving output quality.

RANK_REASON The cluster describes a research paper detailing a new system for prompt formulation.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New AI system PAWNI optimizes prompt formulation for LLMs

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · B. Sankar, Pawni Yadav, Srinidhi Ranjini Girish, Amogh A. S ·

    Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution

    arXiv:2608.01366v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts. Iterative multi-turn prompting often leads to context degradation and diminishing co…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Amogh A. S ·

    Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution

    Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts. Iterative multi-turn prompting often leads to context degradation and diminishing cognitive returns. We present PAWNI (Prompt Architecture Wiz…

  3. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Amogh A. S ·

    Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution

    Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts. Iterative multi-turn prompting often leads to context degradation and diminishing cognitive returns. We present PAWNI (Prompt Architecture Wiz…

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