RAG Workbench
RAG Workbench is a powerful tool designed to streamline the debugging and evaluation process for information retrieval systems. It addresses the common challenges enterprise developers face when optimizing RAG applications, such as managing experiments, debugging issues, and fine-tuning parameters like chunk size, top k, and retrieval strategies.
Key Features
- Distributed Tracing
- Fine-tuned Evaluators
- Debugging & Experimentation
Distributed Tracing
RAG Workbench has first-class support for tracing both retrieval and data ingestion pipelines, allowing you to bridge offline/online systems and identify anomalies with greater accuracy. The tracing SDK is compatible with OpenTelemetry, an open standard protocol for tracing and observability.
Fine-tuned Evaluators
LastMile AI has built in-house evaluator models that represent a step-function jump from the current state-of-the-art, which measure metrics specific to RAG applications, such as hallucination rates, response relevancy and more. The zero-shot models are 1/1000th the cost of GPT-4, and outperform GPT-4 base evaluations.
Experimentation
RAG Workbench has an interactive companion to help developers debug and iterate on any RAG application. Inspect retrieved context, rerun prompts to LLMs within the debugger and have an audit log of calls to your RAG application.
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