Engineering Around Token Limits in Large Codebases

The magic of Large Language Models (LLMs) lies in their ability to understand and generate human-like text, but that magic has a limit—the context window. For developers working on enterprise-scale codebases, this limitation isn’t just a technical constraint; it’s a barrier to using AI effectively. When an AI can’t see your entire project, it starts to guess, leading to errors, hallucinations, and wasted time.

Understanding and engineering around these context limits is the key to unlocking the full potential of AI assistants. It’s how you move from novelty chatbots to building production-ready, AI-powered features that can reason about your entire application. This guide explains what context windows are, why they matter, and the practical strategies you can use to give your AI perfect recall, no matter the size of your codebase.

An AI context window is the amount of information, measured in tokens, that a Large Language Model can process at one time. Think of it as the model’s short-term memory. Everything you provide in a prompt—instructions, questions, examples, and relevant documents—must fit within this window.

A token is the basic unit of data for an LLM, representing a piece of a word. For example, the word “chatbot” might be a single token, but “enterprise” could be split into “enter” and “prise.” On average, a token is about four characters of text in English.

Models have different context window sizes:

• Early models: Could only handle around 2,000 tokens (a few pages of text).
• Newer models: Offer much larger windows, some up to 1 million tokens or more.

While bigger windows help, they don’t solve the core problem for massive codebases, which can contain millions of tokens. Feeding the entire project into the context is often impractical and expensive.

The context window is the AI’s entire world for a given task. If critical information falls outside this window, the model becomes an unreliable partner. It lacks the full picture and starts to “hallucinate”—inventing plausible but incorrect information to fill the gaps.

Imagine asking a developer who has only seen a single file to explain how an entire application works. They might make some educated guesses, but they can’t give you a complete or accurate answer. An LLM with a limited context window operates under the same constraint. This is why you can’t just paste a link to your repository and expect the AI to understand it.

To work effectively with large, private codebases, you need to give the AI the right context at the right time. This involves building systems that find and prioritize the most relevant information for any given task. The three most common strategies are context chunking, semantic search, and Retrieval-Augmented Generation (RAG).

These strategies work together to create a pipeline that transforms your codebase into a long-term memory for the AI.

1. Context chunking: The first step is to break down your vast codebase into smaller, manageable pieces, or “chunks.” Each chunk should be small enough to fit within a model’s context window while still containing a meaningful, self-contained block of code or documentation. This could be a function, a component, or a configuration file.
2. Semantic code search: Once your code is chunked, you need a way to find the most relevant pieces for a specific question. This is where semantic search comes in. Unlike traditional keyword search, which matches exact words, semantic search understands the meaning behind the query. It uses vector embeddings—numerical representations of your code chunks—to find code that is conceptually similar to the user’s prompt, even if it doesn’t use the same keywords.
3. Retrieval-Augmented Generation (RAG): RAG is the system that brings it all together. It takes the user’s query, uses semantic search to retrieve the most relevant code chunks from your vectorized database, and then injects that information into the prompt it sends to the LLM. The AI then uses this curated context to generate a highly relevant and accurate response. A RAG system essentially gives the AI a “just-in-time” memory of your codebase.

Mastering these techniques allows you to build powerful, AI-driven features that were previously impossible.

• Smarter assistants: Create internal chatbots that can answer complex questions about your architecture, API contracts, or deployment processes with high accuracy.
• AI-powered features: Build user-facing features that can reason about a user’s data and behavior, offering personalized experiences or proactive support.
• Automated workflows: Automate engineering tasks like code refactoring, documentation generation, or even identifying potential security vulnerabilities based on the full context of your application.

While powerful, building a custom RAG system for your codebase isn’t a trivial task. The main challenges include:

• Complexity: Setting up a robust RAG pipeline requires expertise in data processing, vector databases (like Pinecone or Chroma), and integrating with LLM APIs.
• Cost: Vectorizing an entire codebase and running queries can be computationally expensive, both in terms of processing power and API costs.
• Maintenance: A RAG system is not a “set it and forget it” solution. As your codebase evolves, you need to continuously update your vector database to ensure the AI is working with the latest information. This is a critical process known as “cache invalidation” for your AI’s memory.

Building sophisticated AI systems requires a solid foundation for your application, including robust user management, authentication, and authorization. While Kinde doesn’t provide AI components directly, its powerful APIs and feature flags make it easier to build, test, and ship AI-powered features securely.

For example, you could use Kinde’s feature flags to safely roll out a new AI assistant to a specific segment of users. You can manage permissions to ensure that only authorized team members can access sensitive internal tools powered by your custom RAG system. By handling the foundational aspects of user management, Kinde lets your engineering team focus on the core challenge: building intelligent, context-aware AI experiences.

For more information on Kinde’s capabilities and how they can support your development process, visit the official documentation:

• Kinde Management API