Wrap-up and next steps

Let's step back and appreciate what you've built. In just a few hours, you went from understanding why LLMs hallucinate to building an AI agent that can search both internal documents and the web to give grounded, sourced answers.

Before taking this to production, consider these important factors: RAG evaluation metrics (precision, recall, faithfulness) to measure quality. Chunk size tuning based on real query patterns. Agent cost management since each reasoning step is an LLM call. Setting max_iterations on your agent to prevent runaway loops. And monitoring to track which tools get used and how often.

Here are four powerful directions to explore: 1. RAG Evaluation, where you measure retrieval quality with RAGAS metrics 2. Advanced Chunking with parent-child chunks, document hierarchies, and auto-merging 3. Multi-Modal RAG to index and retrieve images, PDFs, and tables alongside text 4. Production Deployment covering authentication, rate limiting, caching, and observability