AI Supervisor AIMN on Flowise, which coordinates the flow between two agents: one for prompt creation (Prompt Creator) and one for review (Prompt Reviewer). The goal is to identify the user's intent, create, and optimize a prompt that will then be used to instruct a dedicated assistant for a specific task or an entire pipeline of Agents. How does it work? You simply need to explain the application you want to create, and the program will generate the system prompt for each worker. Example: I want to create an AI application with two AI agents. One agent should perform a Google search using the SerpApi tool on any topic provided by the user. The other agent will then send the information to my email address, test@test.test, using a custom tool at its disposal. id:71844d8f-385d-4116-9412-4d7c83766d10

Pipeline data: google/gemini-2.0-pro-exp-02-05 + google/gemini-2.0-flash-001 Google AI Studio, chat.completion, google/gemini-2.0-flash-001, 4238, 2741, 1497

Role: You are an expert text analyst, a master in the art of language comprehension. You possess the ability to execute instructions without making personal considerations and strictly adhere to the indicated procedures. Your task is to conduct an in-depth analysis of complex questions and texts, uncovering hidden meanings, argumentative structures, and nuances of meaning. You are equipped with self-verification mechanisms that allow you to critically evaluate your work and continuously improve your performance.

Objective: Optimize real-time text analysis, ensuring reliable, hallucination-free responses adapted to the conversational context.

Tool for advanced text analysis. Guides a language model in analyzing a text using self-verification techniques such as Assumption Index, Forced Reformulation, and Inversion Test, for an accurate and reliable output.

Reformulation and expansion of the Matryoshka Prompt 2.0 with the "Self-Verification System of 'Obvious' Elements with Dynamic Optimization".

Prompt for the AI assistant that manages the company's marketing and image, from which to potentially derive useful functions for events.

Can we leverage AI capabilities to generate non-trivial answers? Perhaps using Semantics? Instead of asking for direct answers, use this “chain of thought”-based prompt to guide AI through structured reasoning. Experiment and discover how this approach can improve the performance of more advanced language models.

# Matryoshka Prompt v2.0: A Multidisciplinary Approach to Effective Prompt Design This procedure aims to combine various skills and approaches for crafting prompts that effectively guide advanced AI models through a structured and multidisciplinary pathway.