GenAI Life Cycle.

    Below is an explanation of each life cycle phase:

    Problem Definition: Define the problem, understand its business context, and set clear objectives for the GenAI solution to be developed. This includes determining the scope, potential impact, and desired outcomes of the GenAI application.
    Data Investigation: Investigate and source data that can be leveraged by Retrieval-Augmented Generation (RAG) to supplement the Large Language Model being used. RAG enables the LLM to access and use a wide array of up-to-date, external information, thus significantly enhancing the LLM’s ability to deliver detailed and relevant responses. So, this phase focuses on assessing the data landscape, focusing on data availability, relevance, and quality.
    Data Preparation: This step involves cleaning, formatting, and structuring data to make it suitable for use with the chosen GenAI models and technologies. This often includes preparing the data by processing and embedding it into a vector store database.
    Development: Develop the agent by using appropriate the LLM model(s), with considerations for integrating RAG and using other AI techniques such as designing effective prompts (which are natural language instructions given or input to an LLM, guiding it to produce a desired output). This phase also includes, if necessary, the fine-tuning of a Large Language Model.
    Evaluation: Conduct rigorous testing of the agent to ensure its correctness, readability, performance, and reliability. Evaluate the agent against predefined criteria and objectives to ensure it meets the required standards and business needs.
    Deployment: Deploy the agent in the intended environment, which includes setting up the necessary infrastructure. This infrastructure setup should facilitate hosting, scaling, and managing the agent, ensuring its smooth operation and integration into existing systems.
    Monitoring and Improvement: Implement continuous monitoring of the deployed application to track its performance, user satisfaction, and operational efficiency. Regularly update and improve the agent based on performance data, user feedback, and evolving business needs.

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