Key Takeaways
- Developers are starting to look beyond AI-generated code and are asking a bigger question: can these tools help make sense of what’s already written?
- Some language models are learning to recognize the kinds of patterns that usually show up in well-structured, reliable code.
- Understanding how content ranks in LLMs could be key to building smarter systems that flag messy code, tech debt, or logic that doesn’t quite add up.
- AI could give teams a clearer starting point by highlighting code that’s structurally weak or showing signs of deeper issues, without getting distracted by surface-level styling.
- But the process isn’t perfect. These systems can misread intent, overlook critical context, or surface problems that don’t actually exist.
The Hidden Cost of Untouched Code
You’ve got a growing codebase, commits flying in from all directions, and a backlog full of things that “should probably get cleaned up.” But no one really knows where to start, or which parts of the code are quietly becoming a problem.
Developers already rely on AI to speed things up. We see tools like Copilot that can suggest code in real time, and newer platforms are getting smarter at generating functions based on intent. But what if AI could also take on the opposite task by slowing down, scanning what’s already there, and helping decide what’s worth fixing?
A ranking system could help make sense of it all by scanning the entire codebase and identifying the files most likely to create problems over time. It’s not just based on formatting or syntax, but about spotting fragile logic, inconsistent patterns, and areas where things are starting to slip.
Large language models like ChatGPT are beginning to indicate real potential in identifying signals of code quality, which opens the door to tools that surface high-impact issues and support more focused, efficient development workflows.
The Evolution of AI in Code Workflows
It wasn’t that long ago that autocomplete felt like a breakthrough, suggesting variables, filling in function names, and smoothing out syntax as developers typed. But what started as a convenience has quickly turned into something much bigger. AI is no longer sitting on the sidelines of the coding process, but it’s working alongside developers in real time.
AI coding assistants like GitHub Copilot, Tabnine, and Sourcegraph Cody are changing how developers interact with code. Copilot, built on OpenAI’s Codex model, can generate full code blocks from natural language input, using patterns learned from billions of lines of public code. Tabnine takes a different route, using smaller, fine-tuned models that run locally or in private environments, an option better suited to teams with strict data policies.
Cody, from Sourcegraph, also does something different. Instead of pulling from generic training data, it works with what’s already in your codebase like your documentation, your functions, your history. That context makes its suggestions feel less like templates and more like actual help. It knows what you’ve built and how you’ve built it, which means the recommendations it offers tend to land closer to what you actually need.
Tools like this are starting to feel integrated into the process. They live inside familiar editors like VS Code and JetBrains, offering support as code gets written. Whether it’s a short script or a full-scale feature, these tools remain active throughout the writing process. But when it comes time to review the work and to assess what’s stable and what might introduce risk, they don’t step in. That responsibility still falls to the developer.
Code review takes time as it’s highly detailed work, and even with good habits in place, things get missed. Static analysis tools catch the obvious issues, but they don’t always help with prioritization. What’s still missing is a way to cut through the backlog and surface what actually needs attention.
Why Ranking Code Quality with AI Is Gaining Interest
Figuring out where to focus in a codebase isn’t always obvious. Between feature updates, bug fixes, and years of accumulated shortcuts, it’s easy for real issues to hide in plain sight. Reviews often get done out of habit, looking at the same areas, touching the same files, rather than based on any real signal of risk or instability.
That’s especially true in older systems. Over time, complexity builds. People leave, context gets lost, and the original architecture doesn’t always match how the product has evolved. In those cases, even experienced teams can struggle to pinpoint what’s holding things together and what’s quietly breaking it apart.
This is where ranking systems could offer real value. AI could help focus attention where it’s actually needed—on parts of the code that are starting to show strain. That might be logic that doesn’t hold up anymore, structure that’s hard to follow, or sections that have slowly drifted from how the system is supposed to behave. The goal is not to replace human judgment, but rather sharpens where that judgment is applied.
But for that to work, AI needs a way to evaluate quality beyond style rules or token counts. It needs to weigh structure, logic, and historical usage in a way that surfaces meaningful signals. That starts with understanding the ranking on ChatGPT, how large language models decide what matters based on structure, context, and relevance. More than 80% of organizations now use AI-based ranking to prioritize content, which speaks to how effective these systems can be at surfacing what’s most relevant. Code follows similar patterns. It has logic, dependencies, and usage history that models can learn to weigh.
The more context these systems can process, the more useful their output becomes, especially when there’s too much code and not enough time to review it all manually.
What LLMs Actually “See” When Analyzing Code
LLMs don’t understand code like a human, they see sequences of tokens, embeddings, and patterns. Here’s how that plays out:
Tokenization, Structure, and Embeddings
When you feed code to a model, it needs to break it down into recognizable units, or tokens. These might be keywords (if, while), punctuation ({, };), or even parts of identifiers. Modern LLM tokenizers use approaches like byte-pair encoding or subword tokenization to manage variable names and custom identifiers efficiently.
Once the code is tokenized, it’s mapped into vector representations called embeddings. These capture the structure, meaning, and surrounding context of each piece. So even if two functions look different on the surface, say, def add(a, b): return a + b and def sum(x, y): return x + y—the model can recognize that they behave the same.
What LLMs Pick Up, and What They Don’t
These models are quite good at spotting recurring structures and stylistic patterns, loop constructs, nested conditionals, modular organization. They can generalize across codebases and detect anomalies where patterns deviate.
But LLMs can’t reliably grasp underlying business logic, intent, or deep architectural reasoning; if a function is designed to enforce a security guarantee, that nuance may escape the model.
Mapping Insights Into Ranking
If a model can pick up on where the code starts to drift, whether that’s higher complexity, messy dependencies, or patterns that just don’t fit, it could help assign more weight to those areas. Instead of flagging everything at the same level, AI could bring forward the pieces that break from the norm, pointing to sections that might be harder to maintain or more likely to cause issues down the line.
Research like GALLa (Graph-Aligned Language Models for code) shows that embedding structural context, like AST paths or control flow graphs, can improve how well models detect code issues. Embedding enriched context helps AI assess which code truly stands out and deserves a closer look.
Several tools are already experimenting with ways to assess code quality using a mix of static analysis, AI, and real-time feedback. While most don’t use the term “code scoring” explicitly, they’re moving in that direction by helping developers surface the right issues faster and reduce noise in the process.
Mutable AI is one example. It combines real-time code generation with contextual understanding, aiming to refactor or clean up code as you write. Its suggestions are designed to improve readability and maintainability, not only fix syntax. That focus on structure over syntax hints at a deeper analysis happening below the surface.
Codacy takes a more traditional approach but adds layers of automation. It runs static code analysis across a wide range of languages, highlighting issues by severity and aligning with team-defined standards. While it doesn’t rely on language models directly, it already prioritizes feedback by flagging what’s most likely to affect performance, security, or readability.
Additionally, Sourcegraph’s Cody takes context-aware suggestions even further. By pulling from a repository’s existing code, documentation, and usage patterns, Cody tailors its feedback to the specific project. That makes it a useful step toward more personalized code insights, especially in large codebases where priorities vary across files and teams, and is part of why codebase-aware AI is so powerful.
Together, these tools hint at what’s possible: a future where AI doesn’t just write or lint code, but helps teams decide what needs attention and when.
Pitfalls of Automating Code Judgment
AI can offer helpful signals, but using it to judge code quality comes with risks. Large language models are trained on patterns, not necessarily intent, so it’s not unusual for them to flag valid code as problematic simply because it doesn’t match the styles they’ve seen most often. This can create bias against unconventional, but correct, approaches.
Hallucinations are another concern. LLMs are known to suggest code that looks solid at first glance but doesn’t always work as expected. The problems are often subtle, maybe a condition is off, or a small edge case gets missed. Because the code looks correct, it’s easy to skim past the details. Without a careful review, these kinds of mistakes can end up buried in production and take time to track down later.
Explainability is also limited, if a model ranks a function poorly, developers need to know why. But most systems don’t offer transparency into how that score was determined, making the feedback harder to trust or act on.
Risk of Over-Reliance
Static analysis may now be supplemented with LLM-based insights, but those insights aren’t foolproof. Recent studies show that even when prompted carefully, models still struggle with basic logic, like off-by-one errors or misaligned conditionals.
Human review thus remains essential. These tools can support the process, but they’re not yet ready to replace it.
Building a Productive Feedback Loop
AI becomes more valuable when it learns from real interactions. One of the richest sources of feedback lies in the data developers already generate version history, pull request comments, and review outcomes.
Open-source projects store detailed signals about what reviewers accept, change, or reject. Mining that data helps a model understand which code gets approved and why. Signals like inline comments, approval rates, or request-for-changes notes become training cues.
Research into AI systems that learn from user feedback highlights best practices in capturing these signals cleanly, without overwhelming developers with noise. Let’s say your team constantly adjusts a function to improve readability. When the model recognizes that pattern across dozens or hundreds of changes, it begins to weight readability higher than syntax rules. That makes its ranking more meaningful and tailored to your codebase.
From Insight to Improvement
Graphite’s guide on open-source AI code tools shows how analysis models are already adapting to evolving project standards. Teams using these tools report better code consistency and reduced review fatigue, thanks to smarter, context-aware suggestions.
The loop looks like this: model suggests → developer reviews or ignores → model records outcome → model refines outputs. Over time, that loop transforms a generic scorer into a collaborator that understands your team’s style and priorities, reducing clutter and directing attention where it counts.
A Better Way to Focus
AI doesn’t need to take over the review process to be useful, it just needs to help developers focus. Most teams aren’t necessarily struggling with a lack of data, they’re struggling with where to start. When a model can surface the right parts of the codebase, the ones showing strain, or drifting from how the system is supposed to behave, it gives teams a better way to prioritize.
That only works if the model is trained on the right signals. Not just syntax patterns, but actual feedback: what gets approved, what gets reworked, what reviewers flag again and again. Over time, that kind of loop can help AI understand what clean, reliable code looks like in the context of a specific team.
There’s room for this to become part of the everyday workflow. If it’s built into tools teams already use, whether that’s CI pipelines, internal dashboards, or code review flows, ranking could help guide decisions in the background. Over time, it could ease onboarding, cut down on review noise, and give teams a better shot at staying ahead of growing technical debt.
The goal isn’t automation for its own sake, it’s simple clarity. The kind that helps developers step in sooner, with more confidence, and spend their time on what actually matters.
