In the realm of large-scale software projects, well-maintained documentation is essential for smooth collaboration, efficient onboarding, and effective maintenance. However, the sheer scale and complexity of ever-evolving codebases present unique challenges. While Large Language Models (LLMs) promise to streamline documentation, their limitations in understanding context and generating tailored content often hinder their effectiveness.
This post explores two critical pain points: (1) understanding a large codebase and (2) crafting documentation that serves diverse audiences.
The Scale Problem: Why LLMs Struggle to “See” the Whole Codebase
Large codebases are intricate networks of files, repositories, and development history. Developers rely on extensive cross-referencing, but LLMs, constrained by context windows, often struggle to grasp the full picture.
Details on Fragmentation
Code is rarely self-contained. Dependencies, imports, and function calls require extensive lookups that LLMs may miss. Without a holistic view, documentation can become disjointed and incomplete.
Lost in the Middle Problem
When an LLM’s context window is too small, key information gets ignored, leading to inaccurate or incomplete summaries. This challenge makes it difficult for AI-assisted documentation tools to maintain continuity across extensive datasets.
Refactoring and Legacy Code
LLMs may not recognize patterns or historical context, leading to documentation that misrepresents how the code actually works. This lack of historical awareness can introduce inaccuracies, especially in evolving codebases.
The Audience Problem: Writing for Developers, Managers, and Everyone in Between
Different stakeholders require different types of documentation. One developer may be a French-speaking Python engineer with a back-end focus, while another may be an American Java developer focused on the front end. Additionally, documentation isn’t just for engineers—it serves project managers, security teams, product teams, and external users.
Depth vs. Brevity
Developers need precise function-level documentation, while executives require high-level summaries. Effective documentation must cater to both audiences without overwhelming one or underserving the other.
Terminology & Jargon
LLMs can become inconsistent when switching between technical deep dives and simplified explanations. Without a structured approach, the resulting documentation may lack clarity and coherence.
Style Consistency
Multi-author contributions and AI-generated content can lead to disjointed, sometimes conflicting documentation. Maintaining a unified style and voice is a critical challenge in AI-assisted documentation generation.
Beyond the Hype: Why LLMs Need More Than a Bigger Context Window
Simply increasing token limits doesn’t solve all problems. Documentation requires reasoning, pattern recognition, and consistency.
Retrieval vs. Comprehension
Vector search is useful for fetching information but struggles with interpretation. Effective AI-driven documentation must not only retrieve relevant content but also structure it meaningfully.
Cohesion Across Large Documents
When an AI ingests massive amounts of data, maintaining consistency across thousands of pages becomes a challenge. Ensuring that documentation remains well-structured and cohesive is crucial for usability.
The Future of AI-Assisted Documentation
As AI technologies continue to evolve, better memory, reasoning, and structured context processing will improve documentation accuracy. Advanced tools will be required to manage the scale, audience diversity, and consistency challenges that AI-generated documentation faces today.
Awarity’s Approach to These Challenges
At Awarity, we are actively piloting new technology to enhance AI-assisted documentation. Our Elastic Context Window (ECW) is designed to allow extensive data ingestion and real-time updates, aiming to provide a more comprehensive understanding of complex projects. Unlike traditional LLMs, which are constrained by fixed context windows, our approach is being tested for its ability to analyze larger codebases more effectively.
Addressing the Scale Problem
- Exploring methods to ensure that dependencies, imports, and function calls are captured to provide a holistic view of the software.
- Using the ECW to mitigate the “Lost in the Middle” issue, while minimizing hardware and token costs.
- Evaluating how full codebase analysis can improve documentation accuracy and reflect the evolution of software projects.
Improving Documentation for Diverse Audiences
- Experimenting with ways to tailor documentation dynamically for different stakeholders, from developers to executives.
- Working on maintaining consistency across terminology and style to reduce inconsistencies in multi-author contributions.
- Investigating how both detailed technical documentation and high-level summaries can be structured effectively.
Beyond Expanding Context Windows
- Developing approaches that enhance retrieval by integrating comprehension, ensuring documentation is both accurate and meaningful.
- Testing real-time updates and structured analysis to maintain cohesion across large documentation sets.
As we continue these pilots, we aim to refine our approach and validate its effectiveness. Our goal is to help organizations generate documentation that is accurate, structured, and accessible for all stakeholders involved in software development.
