RAG & Agent Evaluation

What This Guide Covers

• How to build RAG and agentic apps using LangGraph
• What the RAGAS framework is and why it matters
• How to use RAGAS to evaluate context quality, relevance, and factuality
• How to iterate based on metrics to improve performance

🔧 Step 1: Build a RAG + Agent System

Use LangGraph to chain:

• Dense retrieval (e.g., Qdrant, Chroma)
• LLM generation (OpenAI, Claude, Mixtral)
• Agent workflows (tool calling, memory, routing)

📊 Step 2: Use RAGAS for Evaluation

RAGAS (Retrieval-Augmented Generation Assessment Suite) lets you quantify:

• Context Precision: Are the retrieved docs relevant?
• Context Recall: Did we miss any useful context?
• Faithfulness: Are generated answers factually grounded?
• Answer Relevancy: Is the answer directly answering the user query?

🧠 These help measure both the retrieval and generation sides of your app.

⚙️ Step 3: Integrate with LangGraph + LangSmith

• Log RAGAS scores during dev runs
• Use LangSmith traces and feedback annotations
• Benchmark performance over time

🔁 Step 4: Improve with Metrics

Use the insights to:

• Optimize chunking & retrieval strategy
• Tune prompts and model selection
• Refine agent routing and fallback logic

📚 Learn More

• RAGAS docs & tutorials – in-depth guides and integrations https://docs.ragas.io/en/stable/?utm_source=chatgpt.com

• RAGAS GitHub https://github.com/explodinggradients/ragas

• LangGraph Documentation https://docs.langchain.com/langgraph/

• LangSmith Eval Tracing https://smith.langchain.com/