Ragas

Integrate Ragas into the development pipeline alongside frameworks like LangChain or LlamaIndex.

Use Ragas to synthetically generate a diverse dataset of customer queries, relevant document snippets, and ideal answers.

Run periodic evaluations with Ragas metrics (e.g., Faithfulness, Answer Relevancy, Context Precision/Recall) to assess the chatbot's performance on the generated and real test data.

Analyze Ragas scores to identify weaknesses (e.g., poor context retrieval, irrelevant answers) and iterate on RAG pipeline components (e.g., embedding model, retriever, prompt engineering) until desired quality metrics are met.

Deploy Ragas as part of the production monitoring stack for the summarization service.

Configure Ragas to periodically evaluate a subset of live inferences or a curated dataset representing real-world inputs.

Track key Ragas metrics like answer relevancy and faithfulness to the source text over time.

Set up alerts based on thresholds for these metrics. If quality drops below a certain point, an alert is triggered, prompting investigation and potential retraining or model adjustments.

Use the insights from Ragas to systematically improve the model or fine-tune prompt strategies.

Utilize Ragas's synthetic data generation to create a domain-specific dataset of questions and ground-truth answers from financial reports.

Implement different RAG pipelines, each with a distinct LLM or configuration, and run them against the generated dataset.

Apply Ragas's evaluation metrics to quantify the performance of each pipeline across criteria like context accuracy and answer quality.

Compare the Ragas scores of each configuration to objectively determine the most effective LLM and RAG setup for the financial analysis task, justifying architectural decisions with data.