Evaluate, Improve, and Scale Your RAG System
As organizations adopt RAG systems, many find initial implementations fall short. Unlocking RAG’s full potential requires the right models, prompt refinement, and robust retrieval. SuperAnnotate’s platform empowers teams to build better RAG.
Challenges in Building High-Quality Datasets
While many companies look to RAG systems to harness knowledge and improve response accuracy, several challenges often surface. SuperAnnotate partners with clients to tackle obstacles such as these:
Model Choice
Choosing which LLM and embedding model to use can be challenging, especially as high scores on general benchmarks do not translate well to more specific data domains.
System Performance
When RAG systems underperform, it’s typically due to complex, hidden bottlenecks. SuperAnnotate’s evaluations provide clarity, pinpointing specific areas for enhancement.
Embedding Quality
Embedding models excel with robust, domain-specific datasets. SuperAnnotate simplifies the dataset creation process, improving model relevance and efficiency.
Technical Barriers
Many tools are developer-centric, but SuperAnnotate’s platform allows non-technical experts across your organization to contribute effectively, building robust datasets and fostering trust.
Evaluate, Improve, and Scale Your RAG System
SuperAnnotate’s annotation platform empowers teams to optimize RAG systems through data-driven insights and high-quality datasets. By supporting each stage of evaluation and training data collection, SuperAnnotate accelerates the process of enhancing model performance and aligns outputs to meet your organization’s specific requirements.
Adaptable UI
SuperAnnotate’s flexible annotation interface offers complete visibility into each RAG component, including inputs, outputs, and retrieved contexts. This allows reviewers to evaluate, refine, and rank context, capturing critical performance data and generating high-quality datasets that support continuous RAG system improvement.
Integration
SuperAnnotate connects directly to your data and AI platforms, allowing seamless import of RAG user data such as prompts, context, and responses. This streamlined integration supports efficient data transfer and enhances your ability to evaluate and refine model outputs.
Collaborative Workflows
Easily engage multiple experts or even leverage LLMs as evaluators in collaborative workflows. SuperAnnotate simplifies the evaluation process by allowing input from different roles, ensuring your RAG evaluations are informed, diverse, and high-quality.
Keeping Your Data Secure
With SOC2 Type 2 and ISO27001 certifications, SuperAnnotate ensures your data remains secure whether stored in our clouds in the US or EU, or in rest fully within your own environment. Role-based access control and segmentation keep your sensitive data safe across teams and projects.
