This post summarizes key insights from a recent academic paper I co-authored, which investigates methods for handling large codebases using Large Language Models (LLMs). The original paper was written in German and focuses on the technical and structural challenges of applying LLMs in software engineering contexts—especially when working with large repositories.
Overview
The paper, titled “Ansätze zur effizienten Bereitstellung großer Codebasen für Large Language Models”, presents a structured review of current approaches in the field. We focused particularly on Retrieval-Augmented Generation (RAG), chunking strategies, and graph-based code representations to optimize context management.
Key Concepts
1. The Challenge of Context Limits
Even state-of-the-art models like GPT-4 and Gemini 1.5 face limits in processing very large repositories. The “needle-in-a-haystack” problem arises when too much information reduces precision. This is where RAG becomes vital.
2. Retrieval-Augmented Generation (RAG)
Instead of feeding the entire codebase into the model, RAG techniques retrieve only relevant code snippets from a vector or graph database, which are then passed to the LLM. We compared both database types and examined hybrid retrieval methods.
3. Graph-based Code Understanding
We explored representations like:
- ASTs (Abstract Syntax Trees) for code structure,
- Control Flow Graphs (CFGs) for logic paths,
- Program Dependence Graphs (PDGs) for data/control dependencies.
These help maintain semantic relationships across files and enhance LLM comprehension.
4. Use Cases in Software Engineering
The study categorizes LLM use in areas such as:
- Code search
- Autocomplete
- Bug fixing
- Code summarization
- End-to-end generation
Each scenario presents unique retrieval and context balancing challenges.
Diving Deeper into Research Question 3:
How can RAG be applied effectively to analyze software projects with LLMs?
This part of the paper takes a practical look at how RAG is implemented in current research and tooling. We break down the process into four stages: Indexing, Querying, Retrieval, and Generation.
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Indexing: Efficient indexing is crucial. One approach uses graph-based representations like ASTs to preserve structure across files. Another technique involves hierarchical chunking and the creation of metadata-rich vector stores that map the project layout (functions, classes, imports).
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Querying: A key challenge is that user queries are written in natural language, while code is highly structured. To bridge this gap, methods like query rewriting, information injection, and even agent-based enrichment are used. These refine the user’s original prompt to retrieve better-matched code snippets.
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Retrieval: Systems select and assemble only the most relevant pieces of code or documentation. Some setups use multi-step LLM agents that iteratively refine the information retrieved. This can improve accuracy but also increases token usage and complexity.
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Generation: Once retrieval is complete, the selected context is passed to the LLM to generate responses—such as summaries, fixes, or explanations. This stage also involves transforming graph or vector information back into natural text formats.
The research found that agent-based and multi-hop approaches are gaining popularity. These iterative pipelines make retrieval more adaptive, allowing the system to zoom in on exactly what’s needed instead of guessing everything upfront. However, they also come with trade-offs: more LLM calls mean higher cost and the risk of endless loops.
Conclusion
LLMs, when paired with smart retrieval and preprocessing, offer significant potential for handling real-world, large-scale codebases. The RAG paradigm—in all its forms—plays a central role in making these systems scalable and precise. But challenges remain: balancing context length, avoiding information overload, and structuring repositories for semantic retrieval.
Our literature review and analysis suggest that hybrid systems combining structured indexing, semantic retrieval, and iterative agents are the most promising direction forward.
The full academic paper (in German) is available on request.