We're excited to announce the release of "Build My RAG," a new capability in our AI Data Platform (ADaP). This feature helps teams create high-quality Retrieval Augmented Generation (RAG) models with ease.
What is RAG?
Retrieval Augmented Generation (RAG) significantly enhances the capabilities of large language models (LLMs) by leveraging vast external data sources such as your enterprise's knowledge bases. RAG systems provide more reliable and relevant outputs compared to purely generative models, but they are not immune to the pitfalls of poor data quality, which can compromise the reliability of the AI outputs. According to Gartner, poor data quality costs organizations an average of $12.9 million annually due to reworks and inefficiencies.
At Appen, we have interviewed machine learning practitioners and researchers to understand their challenges in developing RAG pipelines. Based on these insights, we propose a data-centric workflow powered by a human-in-the-loop approach designed to guide practitioners through the various stages of the RAG development lifecycle.
Build my RAG: Streamlined Development with Human-in-the-Loop
Build My RAG provides a comprehensive set of templates that cover essential tasks such as deduplicating or extracting data from complex PDFs to ensure scalability for vector database ingestion, enriching source data with tags or annotations from other systems to enhance retrieval quality, and curating golden datasets to aid in the evaluation of end-to-end systems. Check out our demo video.
Steps in the Process
- Prepare My Data - Utilize templates to segment and enrich your documents, ensuring coherent and relevant data for your embeddings.
- Build My Prompts - Design effective prompts using dedicated templates. These templates guide you in crafting questions and commands and help evaluate their quality.
- Optimize My Model - Evaluate, rank, and refine your RAG model's responses to improve accuracy by identifying and correcting discrepancies.
- Make My Model Safe - Ensure robustness and reliability through a rigorous red-teaming process. Use AI chat feedback to test performance and identify potential vulnerabilities.
Templates Make it Easy
Our pre-built templates help you create a highly effective and reliable RAG model tailored to your specific needs. They assist in:
- Evaluating chunks against source documents to ensure completeness, relevance, and integrity.
- Selecting and categorizing information from OCR documents, labeling, and categorizing text using NER, and extracting essential details.
- Summarizing chunks for efficient information retrieval and enriching them with metadata like categories or intents.
- Assigning metadata to raw documents, refining chunk text for accuracy, and deduplicating similar chunks to maintain diverse and distinctive information.
Get Started
Appen's Build My RAG feature, enhanced with human-in-the-loop processes, ensures enterprises can develop high-quality RAG models tailored to their needs. By providing a structured, template-driven approach to data preparation, prompt creation, and model optimization, we help you achieve the accuracy, efficiency, and reliability required for successful RAG implementations.
If you're a current customer, please contact your Appen representative to get started. If you simply want to test drive it, please contact us, and we'll get back to you immediately.