Code Maps: Blueprint Your Codebase for LLMs Without Hitting Token Limits

· origo's blog

If you've tried to use LLMs with large codebases, you've probably experienced this frustrating scenario: paste a few files, hit the token limit, and end up with responses based on incomplete context. It's like trying to explain a city by showing just three random buildings.

But what if there was a better way to represent your code's architecture without sending every implementation detail?

The Context Window Dilemma #

When working with LLMs, context is everything. But we quickly hit a fundamental conflict:

Too little context: The model misses critical architectural information and generates code that doesn't align with your existing patterns.

Too much context: Implementation details consume your precious token budget, pushing out other important information and wasting tokens on code the model doesn't need to see.

This problem becomes particularly acute with large enterprise codebases spanning millions of lines.

Enter Code Maps #

A code map is essentially the "architectural blueprint" of your codebase. It extracts only the structural elements—class definitions, function signatures, type declarations—while omitting implementation details.

 1// === CODE-MAP for UserService.java ===
 2package com.example.service;
 3
 4import com.example.model.User;
 5import com.example.repository.UserRepository;
 6import org.springframework.stereotype.Service;
 7
 8@Service
 9public class UserService {
10  private UserRepository repository;
11
12  public UserService(UserRepository repository);
13  public User findById(Long id);
14  public List<User> findAll();
15  public User save(User user);
16  public void delete(Long id);
17}

RepoPrompt: The Gold Standard for Code Maps #

This concept was popularized by RepoPrompt, which has evolved into the gold standard solution for integrating code maps into your AI workflow. While you can build your own tools (more on that below), RepoPrompt offers a polished, production-ready approach that solves several key challenges:

What sets RepoPrompt apart is its "privacy-first" approach—it works with any model (including local ones), processes your code locally, and integrates via clipboard so your sensitive code never needs to leave your machine.

For teams serious about leveraging AI with large codebases, RepoPrompt eliminates the hassle of building and maintaining your own code mapping tools. The time saved on your first complex refactoring task will likely pay for itself.

What Makes an Effective Code Map? #

A good code map preserves:

And crucially omits:

The result? A representation typically 5-10% of the original code size that captures 90% of what an LLM needs to understand your architecture.

Build Your Own Code Map Generator #

While tools like RepoPrompt offer this functionality commercially, you can build your own code mapper tailored to your tech stack. Here's a simple example for Java using JavaParser:

 1public class CodeMapGenerator {
 2    public static void main(String[] args) throws Exception {
 3        if (args.length == 0) {
 4            System.out.println("Usage: java CodeMapGenerator <file-or-dir>");
 5            return;
 6        }
 7
 8        List<Path> files = new ArrayList<>();
 9        for (String arg : args) {
10            Path path = Paths.get(arg);
11            if (Files.isDirectory(path)) {
12                Files.walk(path)
13                     .filter(p -> p.toString().endsWith(".java"))
14                     .forEach(files::add);
15            } else if (arg.endsWith(".java")) {
16                files.add(path);
17            }
18        }
19
20        JavaParser parser = new JavaParser(new ParserConfiguration().setLanguageLevel(LanguageLevel.JAVA_21));
21
22        for (Path file : files) {
23            System.out.println("// === CODE-MAP for " + file + " ===");
24            try (FileInputStream in = new FileInputStream(file.toFile())) {
25                ParseResult<CompilationUnit> result = parser.parse(in);
26                if (result.isSuccessful() && result.getResult().isPresent()) {
27                    CompilationUnit cu = result.getResult().get();
28
29                    // Print package and imports
30                    cu.getPackageDeclaration().ifPresent(pd -> System.out.print(pd.toString()));
31                    cu.getImports().forEach(id -> System.out.print(id.toString()));
32
33                    // Print public/protected API of all types
34                    for (TypeDeclaration<?> type : cu.getTypes()) {
35                        printTypeAPI(type, 0);
36                    }
37                }
38            }
39        }
40    }
41
42    // Additional helper methods would be implemented here...
43}

Let LLMs Build Your Code Map Generator #

The ironic beauty of this approach? You can use LLMs themselves to build these tools. Here's a prompt that will help you create a code mapper for your language of choice:

Create a code map generator for [LANGUAGE] that extracts the API surface of
source files without implementation details. The tool should:

1. Accept file or directory paths as input
2. Extract and output:
   - Namespace/package declarations
   - Import/include statements
   - Public class/type definitions with inheritance relationships
   - Public method signatures with parameters and return types
   - Public field/property declarations
   - Important annotations/decorators

3. Exclude implementation details like method bodies and private members
4. Format the output in readable [LANGUAGE] syntax

Use the [PARSER_LIBRARY] which is commonly used for parsing [LANGUAGE].
Make the tool runnable from the command line with usage instructions.

Just replace [LANGUAGE] with your target language and [PARSER_LIBRARY] with an appropriate parser:

Why This Approach Works #

Code maps solve several key problems when working with LLMs:

  1. Token efficiency - Fit 5-10x more architectural context in the same token budget
  2. Signal-to-noise ratio - Focus LLMs on relevant structural elements
  3. Holistic understanding - Provide a complete view of system architecture
  4. Cross-file awareness - Help models understand relationships between components
  5. Reduced hallucination - Models are less likely to invent APIs that don't exist

For large enterprise codebases spanning millions of lines, code maps transform what was once impossible—giving LLMs meaningful context about the entire system—into something surprisingly manageable.

Looking Ahead #

Even as context windows grow, code maps will remain valuable. When you have a 1 million token context window, would you rather fill it with detailed implementations of 50 files or a structural overview of your entire codebase? The signal-to-noise ratio will always matter (not to mention the cost!).

By building tools that help LLMs understand our code's architecture without drowning in details, we're creating a middle ground where machines can comprehend our systems at a higher level—more like an experienced developer would—focusing on structure and relationships first, implementation details second.

This approach doesn't just save tokens—it fundamentally changes how effectively LLMs can reason about complex software systems.