Methodology
Theia Task Models is constructed using prompt and data engineering. We introduce these novel concepts including prompt engineering, RAG, and model self-tuning, which is used to build Theia Task Models.1. Prompt Engineering
Prompt engineering refers to a series of instruction for Theia. It can trigger Theia to wake up specific capability in some field and work as a task model as instructed. The instruction should be clear and the output format can be defined. To build a good task model, we normally tell Theia three important messages: role, hint for reasoning and generation, and goal. For example, to build a crypto investment advisor, we can write the prompt including the three messages:- Role: Theia, you are an investment advisor.
- Hint: Whenever I provide you a whitepaper of a project, please analyze the whitepaper and search the news of this project.
- Goal: Then generate an investment advisory report.
2. Retrieval-Augmented Generation (RAG)
RAG is a method to integrates retrieval-based models with AI models, utilizing the intelligence of AI to analyze data and generate results according to specific objectives. It mainly consists of three steps:- Data Retrieval: Retrieve relevant information from external datasets, e.g., the price variation of BTC in the past 3 months.
- Task Objective: Define the objective of RAG by prompt engineering, e.g., “find the buying and selling signals according to the MACD”.
- Augmented Generation: Input the retrieved data into the Theia Task Model to get the augmented results.