I wanted to understand how multi-hop RAG works and see if DSPy lives up to the hype. Built a demo with the MuSiQue dataset to explore iterative retrieval with synthesis-in-the-loop. Here's what I learned about adaptive reasoning and why it makes a real difference.
How an agentic RAG approach uses context engineering (not vector search) to let LLMs retrieve and reason over complex enterprise data in natural language.
Tired of tweaking prompts? Discover DSPy, the framework that shifts LLM development from fragile prompt engineering to robust, optimizable programming, and see how it radically simplifies building complex AI systems.