RAFT (RAG + Fine-tuning) in Azure AI Studio

Introduction

Introduction

Welcome to the RAFT hackathon! This International initiative features a series of live streams running from September 3rd to 16th, aimed at educating participants on implementing Retrieval-Augmented Generation (RAG) setups using Azure AI Studio. The RAFT methodology combines RAG with fine-tuning techniques, improving the performance and relevance of responses generated by language models. Throughout this event, over 25 live sessions will guide you through building RAG applications using various Microsoft Technologies, with a chance to win one of ten cash prizes for the most innovative projects.

Session Overview

Code of Conduct

Before diving into the specifics of RAFT and our RAFT-enabled workflows, we’d like to emphasize the importance of maintaining a respectful environment for both participants and presenters. Engagement in the chat is encouraged, but please keep your commentary professional and relevant. All sessions will be recorded and available for on-demand viewing afterward.

Understanding RAG

Retrieval-Augmented Generation (RAG) is a powerful technique that enhances the capabilities of language models by allowing them to answer questions with data specific to a given domain. By querying a set of documents stored in a database and retrieving relevant information, RAG can ground the results and provide more accurate answers based on the content retrieved.

However, challenges remain with RAG setups. Sometimes, irrelevant documents can be retrieved, leading to misleading answers. The RAFT methodology was developed to address these issues, optimizing retrieval and improving the overall process.

Introducing RAFT

RAFT stands for Retrieval-Augmented Fine-Tuning, combining the strengths of fine-tuning pre-trained language models with RAG techniques. The process begins with data preparation, specifically the curation of a dataset designed for question and answer scenarios.

Fine-tuning a model on domain-specific knowledge enables better performance in handling jargon, acronyms, and other unique phrases not typically found in general language models. The methods employed in RAFT also focus on ignoring irrelevant information while emphasizing important context to improve output accuracy.

Implementation Process in Azure AI Studio

Here’s a brief overview of the steps involved in implementing RAFT:

1. Synthetic Data Generation: Using a base language model, generate questions and answers pertaining to your documentation. The RAFT technique harnesses multiple methods, including the Chain of Thought, to derive meaningful and insightful responses.

2. Fine-Tuning: Fine-tune your pre-trained model with a dataset obtained from the previous step. This involves customizing the model to gain domain-specific insights while potentially speeding up execution and reducing costs.

3. Model Deployment: Deploy the fine-tuned model into Azure AI Studio, where it can be integrated into applications for real-time retrieval and generation tasks. This phase includes verifying the model’s functionality and performance.

4. Evaluation: Finally, evaluate the fine-tuned model to assess its output quality compared to expected results. This also includes identifying any potential areas for further improvement.

Cost Considerations

While implementing RAFT can substantially improve the effectiveness of your applications, understanding associated costs is crucial. Typically, costs arise from data generation, model fine-tuning, and inference operations.

Azure AI Studio provides infrastructure for cost estimation, although users may need to calculate specific costs manually based on their needs and the model configurations selected.

Key Takeaways

RAFT is an innovative method that drives improvement in RAG setups by combining pretrained language models with custom fine-tuning processes. Organizations can leverage these capabilities to create robust applications capable of responding to domain-specific queries with increased accuracy and efficiency.

Keyword

RAFT, RAG, fine-tuning, Azure AI Studio, synthetic data generation, deployment, evaluation, cost estimation, model performance.

FAQ

Q1: What is RAFT in Azure AI Studio?
RAFT stands for Retrieval-Augmented Fine-Tuning which combines RAG techniques with the fine-tuning of pre-trained language models to improve context-aware responses.

Q2: How does RAFT improve performance over traditional RAG?
RAFT utilizes domain-specific data to retrain models, allowing them to better handle specialized terms and ignore irrelevant noise in the document retrieval phase.

Q3: What are the main steps in implementing RAFT?
The major steps include synthetic data generation, fine-tuning the model, deploying it to Azure AI Studio, and evaluating its performance.

Q4: Are there any cost implications associated with implementing RAFT?
Yes, costs are associated with data generation, model fine-tuning, and inference operations. While Azure AI Studio has pricing structures, precise cost calculations may need to be performed manually.

Q5: Can RAFT be used with non-English languages?
Yes, RAFT can be effectively employed with non-English languages, although it is worth noting that the performance may vary based on the language model's existing bias toward English language knowledge.