Software bugs are a costly problem, and formal verification, while effective, is time-consuming and requires expertise. Imagine a world where proving software correctness is automated. Rango, a new AI-powered tool, makes this a reality. By leveraging large language models (LLMs) and a clever retrieval system, Rango can automatically synthesize proofs in the Coq proof assistant, dramatically reducing the manual effort involved in formal verification. Unlike previous tools, Rango doesn't just rely on identifying relevant lemmas and definitions. It also learns from similar proofs within the same project. This innovative approach, called retrieval-augmented proving, allows Rango to adapt to the specific project and the evolving proof as it's being constructed. Think of it like having an AI pair programmer that specializes in proofs. At each step, Rango looks at the current proof, retrieves similar proofs and relevant lemmas from the project, and uses this information along with an LLM to suggest the next step. This makes it far more effective than tools that simply try to guess the next tactic. Tests on a massive new dataset called CoqStoq show that Rango outperforms state-of-the-art tools, proving 29% more theorems than the previous leader. The research demonstrates how LLMs, combined with smart retrieval techniques, can transform complex tasks like formal verification, making high-quality, bug-free software more attainable. While challenges remain, such as handling very long or complex proofs, Rango's adaptive learning represents a significant leap forward in automating software verification and paves the way for a future where proving software correctness is as easy as writing it.