Auto-blockprompt

Inspiration

The inspiration behind "auto-blockprompt" emerged from the necessity to automate the creation of effective prompts for querying blockchain data. We aimed to develop a system that autonomously generates optimal prompts for interacting with blockchain data efficiently and accurately.

What it does

"auto-blockprompt" is an automated prompt engineering system designed to facilitate the generation of prompts for blockchain queries. Leveraging a genetic algorithm and a Large Language Model (LLM), it crafts optimized prompts for querying blockchain data, streamlining the interaction process.

How we built it

We built "auto-blockprompt" by first defining the genetic algorithm's components, including the space of potential prompts and their gene structures. The fitness function was developed to evaluate prompt effectiveness by comparing LLM-generated outputs to desired blockchain query results. Integrating the genetic algorithm with the LLM was a critical part of the development process.

Challenges we ran into

Throughout the development process, we encountered challenges in designing an efficient fitness function that accurately assessed prompt effectiveness. Optimizing the genetic algorithm's parameters and ensuring seamless integration with the LLM posed significant challenges.

Accomplishments that we're proud of

We take pride in successfully implementing a functional automated system that generates optimal prompts for blockchain queries. Achieving a seamless integration between the genetic algorithm and the LLM to produce effective prompts stands as a significant accomplishment.

What we learned

The development of "auto-blockprompt" provided insights into the complexities of prompt engineering for blockchain data queries. We gained valuable knowledge about optimizing genetic algorithms, evaluating fitness functions, and integrating large language models into automated systems.

What's next for auto-blockprompt

Moving forward, our focus for "auto-blockprompt" involves enhancing its capabilities to handle diverse and complex blockchain queries. We aim to refine the genetic algorithm, explore advanced AI techniques for prompt optimization, and improve the interaction between the system and blockchain data sources. Additionally, we plan to integrate it into HyperCycle’s Computation Node software for broader usability and scalability.

Built With

• blockchain
• cryptography
• flask
• google-generative-ai