Developing and Serving RAG-Based LLM Applications in Production

Step-by-Step Tutorial: Developing and Serving RAG-Based LLM Applications in Production

Overview:

In this tutorial, we will guide you through the process of developing and serving RAG-Based LLM (Language Model) applications in production based on the insights shared in the YouTube video titled "Developing and Serving RAG-Based LLM Applications in Production" from the channel Anyscale.

Step 1: Building the RAG Application

1. Start by building a RAG (Retrieval-Augmented Generation) application, which is a canonical use case for many tech companies to make it easier for users to work with their products.
2. Utilize the capabilities and functions of Ray to help developers work faster and better.
3. Ensure you have the necessary underlying documents, such as data sources, documentation, and user questions, to facilitate building the application effectively.

Step 2: Chunking Logic and Data Preparation

1. Begin by chunking your data sources, such as Ray documentation, using a more efficient strategy than random chunking.
2. Consider using sections of HTML documents to chunk data effectively, providing references and context for better organization.
3. Experiment with different chunking strategies to find the most efficient approach for your application.
4. Load the chunked data into a Vector database, including text, source, and embeddings.

Step 3: Choosing a Vector Database

1. Evaluate different options for Vector databases, such as PostgreSQL, based on your familiarity, team preferences, and specific application requirements.
2. Consider the scalability, features, and performance of the Vector database to ensure it aligns with your application needs.

Step 4: Retrieval Workflow and LM Evaluation

1. Embed queries using an embedding model and retrieve top K contexts using distance metrics like cosine similarity.
2. Feed the retrieved contexts and query text into the base LM to generate a response.
3. Evaluate different LM models, such as GPT-4, GPT-3, and Turbo, to determine the most suitable evaluator for your application.
4. Train a classifier to route queries to the appropriate LM based on the evaluation results.

Step 5: Cost Analysis and Component Tuning

1. Analyze the quality scores and costs of different LM models to find a balance between performance and affordability.
2. Fine-tune components like chunk size, number of chunks, embedding models, and LM choices to optimize application performance.
3. Consider using a hybrid LM routing approach to combine the strengths of different LM models for improved results.

Step 6: Iteration and Documentation Improvement

1. Iterate on your application by gathering feedback, making improvements, and testing different configurations.
2. Use the application to improve documentation quality, identify errors, and enhance user experience.
3. Focus on continuous iteration to cover a wide range of use cases and minimize manual intervention in the long run.

By following these steps, you can effectively develop and serve RAG-Based LLM applications in production, leveraging the insights shared in the YouTube video to create efficient and user-friendly products.