Imagine having a coding assistant that anticipates your needs, fetching relevant code snippets and documentation as you type. Researchers are bringing this vision closer to reality with CONAN, a retrieval-augmented language model designed to mimic how human developers search for knowledge while coding. Unlike traditional code generation models that rely solely on their internal knowledge, CONAN actively seeks external resources, similar to how a developer might consult Stack Overflow or GitHub. This approach addresses the limitations of current AI models, which often struggle with complex coding tasks due to their bounded knowledge base. CONAN consists of two key components: a retriever and a generator. The retriever, CONAN-R, is trained to understand code structure and find relevant information in a vast database of code and documentation. It uses innovative techniques like Code-Documentation Alignment and Masked Entity Prediction to learn more effective code representations, reducing noise and retrieving highly pertinent results. CONAN-G, the generator, receives these targeted results and weaves them into the code generation process. It uses a clever dual-view approach, treating documentation as a summary or 'gist' to guide the model's understanding of the retrieved code. This helps CONAN generate higher-quality code, even for lengthy and complex tasks. But CONAN isn't just for individual developers. It can also supercharge large language models (LLMs), providing them with summarized and denoised external knowledge to enhance their coding abilities. Testing shows that CONAN outperforms existing code generation models, particularly for longer and more complex tasks. It demonstrates marked improvements in code generation, summarization, and completion, effectively acting as an intelligent coding companion. While CONAN shows great promise, there are challenges ahead. Further research is needed to refine its retrieval and generation processes and ensure it can handle the nuanced demands of real-world coding scenarios. As AI models continue to evolve, CONAN points towards a future where coding becomes a more collaborative and intuitive process, assisted by intelligent AI companions that understand our needs and augment our capabilities.