Synthetic Data Generation for RAG & Agent Evaluation
Learn how to generate high‑quality synthetic datasets using LLMs like GPT‑4 to evaluate and benchmark Retrieval‑Augmented Generation (RAG) and agentic AI systems efficiently and at scale.
A comprehensive exploration of synthetic data approaches:
✅ LLM‑powered Generation: Use GPT‑4 or similar models to create rich QA pairs and structured evaluation datasets https://www.confident-ai.com/blog/the-definitive-guide-to-synthetic-data-generation-using-llms?utm_source=chatgpt.com
🛠 Frameworks & Tools: Step-by‑step tutorials using frameworks like DeepEval, RAGAs, and LangSmith for dataset synthesis and automated evaluation https://docs.smith.langchain.com/evaluation/tutorials/evaluation?utm_source=chatgpt.com
🌐 Tutorial Repos & Guides: Hands‑on code from repositories like LangChain-SynData-RAG-Eval, detailed blog posts (e.g. AWS Bedrock, Medium), and open‑source docs on RAG evaluation using synthetic corpora https://github.com/mddunlap924/LangChain-SynData-RAG-Eval?utm_source=chatgpt.com
📈 Best Practices: Guidance on dataset curation, prompt design, filtering, “LLM-as-a-judge” evaluation, and iterative refinement workflows https://www.evidentlyai.com/llm-guide/llm-test-dataset-synthetic-data?utm_source=chatgpt.com