How to Evaluate RAG Applications | Build, Evaluate & Improve in Under 30 Lines of Code

Description

Most RAG apps look fine until a user catches the hallucination. In this video, we build a RAG customer support bot, watch it fail, and run real evaluations on it using FutureAGI’s eval library. Context relevance, groundedness, hallucination detection all scored, all explained, in under 30 lines of code. 🔗 Try Future AGI free: https://futureagi.com 📅 Book a Demo: https://futureagi.com/contact-us 💬 Join our Discord: https://discord.gg/futureagi 🧠 What You’ll Learn - Why RAG fails silently — wrong retrieval, ignored context, and confident hallucinations - The difference between retrieval failure and generation failure and why it matters - How to score your pipeline with 5 metrics: context relevance, groundedness, hallucination, chunk utilization, and completeness - How to interpret low scores and know exactly which part of your pipeline to fix - How to run FutureAGI evals in your code with a single function call per metric 📊 The 5 Metrics ▸ Context Relevance — Did the retriever fetch useful chunks? └ Low score = Fix your retriever or chunking ▸ Groundedness — Does the answer stay within the context? └ Low score = Fix your system prompt / guardrails ▸ Detect Hallucination — Did the model invent facts? └ Low score = LLM ignoring context — add constraints ▸ Chunk Utilization — Did the model actually use the chunks? └ Low score = Irrelevant chunks are being retrieved ▸ Completeness — Did the answer cover everything asked? └ Low score = Missing docs in knowledge base 🔗 Resources Mentioned Future AGI Platform: https://bit.ly/3PkuKyc Future AGI Docs: https://bit.ly/4rKf5Wc Future AGI on GitHub: https://bit.ly/40M1H95