Formal verification, a rigorous method for ensuring software correctness, holds immense promise for building safer and more reliable systems, especially in critical domains like aerospace or medicine. But crafting formal proofs, particularly in proof assistants like Coq, is notoriously complex and time-consuming. This is where CoqPilot, a groundbreaking VS Code extension, steps in. Imagine an AI autopilot guiding you through the intricate process of proof construction, automatically filling in the gaps and streamlining the entire verification workflow. CoqPilot leverages the power of Large Language Models (LLMs) like GPT and Claude, combined with non-ML techniques, to suggest proof candidates for 'holes' in incomplete Coq proofs. It dynamically retrieves relevant context from existing proofs, providing the LLM with valuable background information. This 'few-shot' learning approach enhances the accuracy and relevance of the generated proofs. What sets CoqPilot apart is its integrated proof-checking mechanism. It interacts with the Coq system to verify generated proofs, ensuring that only valid solutions are presented to the user. CoqPilot doesn’t stop there. If a suggested proof fails, a specialized multi-round interaction with the LLM kicks in, leveraging error messages to refine the proof candidate. This iterative process mirrors a human expert’s approach to proof construction, leading to progressively improved solutions. Benchmarks on a real-world project demonstrate CoqPilot's ability to significantly boost the proof-generation capabilities of LLMs. The combination of multiple LLMs with traditional tools like Tactician and CoqHammer via CoqPilot further amplifies its effectiveness. While the results are impressive, challenges remain. Token limitations of current LLMs restrict the amount of context that can be used. Future work involves optimizing premise selection, refining error-driven refinement strategies, and integrating even more powerful LLMs. CoqPilot represents a significant stride toward democratizing formal verification. By automating the laborious aspects of proof construction, it empowers developers to create provably correct software, paving the way for a future where software bugs in critical systems become a relic of the past.