Build a Retrieval-Augmented Coding Assistant in 20 Minutes

Retrieval-Augmented Generation (RAG) is a technique that enhances the accuracy and relevance of large language models (LLMs) by grounding them in specific, private, or real-time information. For engineering teams, this means creating AI assistants that are not just general-purpose coding aids, but deeply knowledgeable partners, fluent in your team’s specific codebase, documentation, and architectural patterns.

A RAG-based assistant can answer questions, write code, and even suggest architectural improvements with a level of context that public LLMs like GPT-4 or Claude can’t match on their own. By connecting a model to your own data sources—like your company’s GitHub repositories, internal wikis, or API documentation—you create a powerful, project-aware agent.

This guide will walk you through the what, why, and how of building a simple RAG-powered coding assistant, helping you turn the concept into a practical tool for your team.

A RAG system combines the information retrieval capabilities of a search engine with the generative power of a large language model. This process ensures the model’s responses are not just fluent, but also factually grounded in your specified data. The workflow can be broken down into three core steps: indexing, retrieval, and generation.

1. Indexing and embedding The system first needs to build a searchable knowledge base from your private data sources. This involves:
   • Data ingestion Connecting to and pulling data from specified sources, such as Git repositories, Confluence, Notion pages, or even Slack conversations.
   • Chunking Breaking down large documents and code files into smaller, semantically coherent pieces, or “chunks.” This makes the information easier to search and more relevant to specific queries.
   • Embedding Using a special type of AI model called an embedding model to convert these chunks into numerical representations (vectors). These vectors capture the semantic meaning of the text, allowing the system to find related information even if the keywords don’t match exactly.
   • Storing Storing these vectors in a specialized vector database, which is optimized for fast similarity searches.
2. Retrieval When you ask your coding assistant a question (a “prompt”), the RAG system swings into action to find the most relevant information from your knowledge base.
   • Embedding the query Your question is also converted into a vector using the same embedding model.
   • Similarity search The system then searches the vector database to find the chunks of text whose vectors are most similar to your query’s vector. This is the “retrieval” part of RAG.
   • Context augmentation The most relevant chunks of text are gathered and prepared to be sent to the LLM. This retrieved information becomes the “context” that the model will use to inform its answer.
3. Generation The final step is to generate a human-like answer.
   • Augmented prompt The original prompt is combined with the retrieved context and sent to an LLM (like GPT-4, Llama 3, or Claude 3).
   • Synthesizing the answer The LLM uses this augmented prompt to generate a response that directly addresses the user’s query while being grounded in the provided context. This prevents the model from hallucinating or providing generic, unhelpful advice.

For example, if you ask, “What’s the best way to add a new component to our design system?” the RAG system will retrieve documentation about your design system’s architecture, find code examples of existing components, and feed all of it to the LLM. The model then synthesizes this information into a precise, actionable answer that reflects your team’s established patterns.

Integrating a RAG assistant into your engineering workflow can solve many common challenges related to knowledge sharing, onboarding, and code quality. By providing instant, context-aware answers, it acts as a force multiplier for your team. Here are some key use cases and applications.

• On-demand repo Q&A Instantly get answers about your codebase without having to manually search through files or disturb colleagues.
  • “What’s the schema for the users table?”
  • “Where is the authentication logic handled in our Next.js app?”
  • “Show me an example of how we handle API errors.”
• Faster, more consistent onboarding New engineers can get up to speed quickly by asking the assistant questions about the codebase, development environment, and team conventions. This reduces the burden on senior engineers and ensures new hires learn the right way to do things from day one.
• Architectural and design decisions When you’re exploring a new feature, you can consult the assistant for insights based on your existing architecture.
  • “What’s our preferred library for state management in the frontend?”
  • “Summarize the discussion in the architecture review doc for the new billing service.”
• Summarizing documentation and internal wikis Quickly get the gist of long or complex documents without having to read them from start to finish.
  • “What are the key takeaways from the Q3 2025 roadmap planning document?”
  • “Summarize our incident response protocol.”

While powerful, building a production-ready RAG system comes with its own set of challenges. It’s important to be aware of these hurdles before you start. Here are some common things to watch out for.

Building a RAG assistant is an iterative process. Start small, focus on a high-value use case, and expand from there. Here are some best practices to guide you.

• Start with a narrow set of high-quality data sources Don’t try to boil the ocean by connecting every possible data source at once. Start with a single, well-maintained source, like your primary API documentation or a specific GitHub repository. This will make it easier to fine-tune your chunking and retrieval strategies.
• Implement robust access controls from day one Security should not be an afterthought. If your assistant will be used by multiple people, make sure it respects the underlying permissions of the data sources. A user shouldn’t be able to get answers about a private repository they don’t have access to.
• Automate your data synchronization To be useful, your assistant’s knowledge must be current. Use tools like GitHub Actions or webhooks to automatically update your vector database whenever your documentation or code changes.
• Log and review user queries Pay attention to the questions users are asking. This will give you valuable insight into what information is most needed and where your documentation might be lacking. It will also help you identify areas where your RAG system is underperforming.

Building a custom RAG solution often means building a custom application to host it. This is where Kinde can help. Kinde provides the authentication and user management infrastructure needed to secure your internal tools, ensuring that only authorized team members can access your R.A.G. assistant and its underlying data.

With Kinde, you can:

• Quickly add secure sign-in to your custom RAG application.
• Use organizations to manage different teams or departments.
• Control access to sensitive data sources by mapping user permissions to your RAG system.

By handling the authentication and authorization, Kinde lets you focus on what matters most: building an intelligent assistant that empowers your engineering team.

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