Background

Due to the boom of LLM, we have seen more and more AI + Blockchain projects recently. Besides the combination of LLM and blockchain, we also see AI coming back to the blockchain. ZKML is one of the popular combinations.

AI and Blockchain are two different technologies with totally different characteristics. AI needs large computation power which is offered by centralized data centers. Blockchains provide decentralized computation and privacy but are not good at large computation and large storage. We are still exploring the correct way to combine AI and blockchain. Here is an overview of AI + Blockchain projects.

This map is also used in our OFR activity

In this research piece, we will mainly focus on the application of LLM in the crypto space. LLM is really powerful tech because of its ability to understand natural language and developers are using LLM in these two directions:

• Provide accurate and up-to-date answers over a large amount of context data
• Complete specific tasks by using different agents/tools

Here is an engineering workflow diagram for building an LLM app to answer users’ questions. First, related data sources are generated into embeddings and stored in the vector database. The LLM adapters use user query and similarity search to find related contexts from the vector database. The related contexts are put into the prompt and sent to the LLM. LLM will execute the prompts and use the tools it has to generate the response. Sometimes the LLM is tuned on specific datasets to improve accuracy and cut the cost.

The LLM application workflow can be generally categorized into three main phases:

• Preparation and Embedding of Data: This phase involves the preservation of confidential information (for instance, project memos) for future access. Commonly, the files are fragmented, processed through an embedding model, and then saved in a particular kind of database known as a vector database.
• Formulation and Extraction of Prompts: When a user submits a search request (in this case, a search for project information), the software creates a sequence of prompts to input into the language model. A final prompt generally fuses a prompt template that is hard-coded by the software developer, examples of valid outputs referred to as few-shot examples, any required data obtained from external APIs, and a selection of relevant files extracted from the vector database.
• Execution and Inference of Prompts: After the prompts are finalized, they are given to a pre-existing Language Model (LLM) for inference — which may include both proprietary model APIs and open-source or individually fine-tuned models. Some developers might also incorporate operational systems such as logging, caching, and validation at this point.

We come up with 8 potential directions in which LLM can help the blockchain space:

• Integrate built-in Al/LLM functions into the Blockchain
• Use LLM to analyze Transaction Records
• Use LLM to identify Potential Bots
• Use LLM to write codes
• Use LLM to read Code
• Use LLM to help the community