Evaluating Code Data Sources for Training Large Language Models

Introduction

The quality of a coding LLM is only as good as its training data. While billions of lines of code are publicly available, not all data sources are created equal. The choice between open-source repositories, educational content, proprietary datasets, and dedicated human-written code has a direct impact on model performance, licensing risk, and real-world usefulness.

Building a coding LLM involves navigating a complex landscape of trade-offs. Do you prioritize volume over quality? Free data over licensed? Speed of collection over curation rigor? These decisions compound — the wrong data mix can result in a model that writes syntactically correct but practically useless code, or one that exposes your organization to legal risk from improperly licensed training data.

This guide compares the five major categories of code data sources and provides a practical framework for choosing the right mix based on your model's goals, budget, and risk tolerance.

1. Public Open-Source Repositories

Platforms like GitHub, GitLab, and Bitbucket host billions of lines of code in virtually every programming language. Major datasets like The Stack (by BigCode), StarCoder Training Data, and CodeParrot have made this code accessible for LLM training at scale. GitHub alone has over 200 million repositories, providing an enormous corpus of real-world code.

Advantages

The sheer volume is unmatched — no other source comes close in terms of raw quantity. Language coverage is extremely broad, from mainstream languages like Python, JavaScript, and Java to niche ones like Haskell, Rust, or COBOL. The data is freely accessible and constantly growing as developers push new code every day.

Open-source code also captures real-world development patterns — production systems, library implementations, tooling, infrastructure code. This diversity helps models generalize across different coding styles, paradigms, and use cases.

Challenges

Quality is highly variable. Studies on the StarCoder training data found approximately 38.6% near-duplicate content — meaning more than a third of the data is essentially redundant. Beyond duplicates, open-source repos contain abandoned projects, student homework, auto-generated boilerplate, copied Stack Overflow snippets, and code with known vulnerabilities.

Licensing is a significant concern. Repositories may be licensed under GPL (requiring derivative works to be open-sourced), MIT (permissive), Apache, or have no license at all. Using GPL-licensed code to train a commercial model is legally risky. Projects like The Stack V2 have attempted to filter by license, but license detection is imperfect — many repos lack proper license files, and sublicensing through dependencies creates complex chains.

Preprocessing is labor-intensive. To get usable training data from raw repositories, you need to: deduplicate at both file and function level, filter by license, score code quality (removing minified code, generated files, data dumps), handle multiple programming languages with different syntax, and deal with encoding issues, binary files, and extremely large files.

Best For

Pre-training large base models where volume is the primary concern. Open-source data provides the broad foundation that helps models learn syntax, common patterns, and the general structure of code across languages. It's the starting point for almost every coding LLM, but rarely sufficient on its own for producing a high-quality model.

2. Curated Educational Content

University assignments, MOOC exercises (Coursera, edX, Udacity), coding bootcamp projects, and textbook examples represent a distinct category of code. This content is created with a pedagogical purpose — it's designed to teach concepts clearly, demonstrate best practices, and build understanding step by step.

Advantages

Educational code tends to be clean, well-commented, and well-structured. It follows a clear problem-solution format that's excellent for teaching models how to reason about code. Comments explain the "why" behind decisions, not just the "what." Variable names are descriptive. Functions are well-decomposed. Error handling is explicit.

This type of code is particularly valuable for training models that need to explain code, generate tutorials, or assist in learning environments. The pedagogical structure — problem statement, approach, implementation, explanation — maps naturally to the kind of prompt-response pairs that coding assistants produce.

Educational content also tends to demonstrate canonical solutions to well-known problems: sorting algorithms, data structures, design patterns, web application scaffolding. This gives models a strong foundation in fundamental programming concepts.

Challenges

Scale is limited compared to open-source repositories. The total volume of educational code is orders of magnitude smaller. Coverage tends toward beginner and intermediate topics — advanced system design, performance optimization, and production-grade patterns are underrepresented.

Licensing varies significantly by institution. Some universities make courseware freely available under Creative Commons, while others retain strict copyright. MOOC platforms have their own terms of service that may restrict data use. Scraping educational content without proper licensing is both legally and ethically problematic.

There's also a risk of overfitting to "textbook" patterns. Code trained heavily on educational content may produce clean but simplistic solutions that don't account for real-world complexity — edge cases, backwards compatibility, integration with existing systems, performance under load.

Best For

Fine-tuning models for code explanation, tutoring applications, and educational assistants. Also valuable as a quality signal during training — educational code can help "pull up" the overall quality of a model trained primarily on noisier open-source data.

3. Proprietary & Commercial Datasets

A growing number of vendors sell curated, high-quality code datasets specifically designed for LLM training. These datasets are typically cleaned, deduplicated, annotated, and come with clear licensing terms. Companies like Scale AI, Surge AI, and others offer both off-the-shelf and custom datasets.

Advantages

The primary advantage is quality assurance. Commercial datasets go through rigorous curation: deduplication, quality scoring, syntactic and semantic validation, and often human review. They frequently include rich metadata — complexity ratings, domain tags, language version annotations, and test coverage information.

Licensing is clean and explicit. When you purchase a commercial dataset, the license terms are clear, and the vendor has typically done the work of ensuring that the underlying code is properly licensed for your intended use. This removes a major legal risk that comes with scraping open-source repositories.

Some commercial datasets also include paired data — code with natural language descriptions, function signatures with implementations, bug-fix pairs, or code review annotations. These paired examples are particularly valuable for training instruction-following and code completion models.

Challenges

Cost is the biggest barrier. High-quality curated datasets can cost tens of thousands to hundreds of thousands of dollars, depending on scale and specificity. For startups or research teams with limited budgets, this may be prohibitive.

Transparency is sometimes limited. Not all vendors are forthcoming about their curation methodology, the original sources of the code, or the specific quality criteria applied. Without this transparency, it's hard to assess whether the dataset truly meets your needs or contains hidden biases.

Coverage may be narrow. Commercial datasets tend to focus on popular languages (Python, JavaScript, Java, C++) and common domains (web development, data science, mobile). If you need training data for niche languages (Elixir, Nim, Zig), specialized frameworks, or domain-specific patterns (embedded systems, HPC, blockchain), commercial options may be limited.

Best For

Organizations that need licensing certainty and want to minimize preprocessing overhead. Also a good choice for teams that have the budget and want to accelerate model development by starting with high-quality data rather than building their own curation pipeline from scratch.

4. Freelance & Crowdsourced Code

Hiring freelance developers to write code for specific tasks or domains is an increasingly popular approach for generating targeted training data. Platforms like Upwork, Toptal, and specialized data labeling services can provide access to developers across a wide range of skill levels and language specializations.

Advantages

The biggest advantage is targeting. You define exactly what gets written — the languages, the problem types, the difficulty level, the coding style, the documentation requirements. This is invaluable for filling specific gaps in your training data. Need 500 examples of asynchronous error handling in Rust? Complex SQL query optimization examples? Kubernetes configuration for edge cases? You can commission exactly that.

You also get full IP ownership. Unlike open-source code with mixed licensing, code written by freelancers under a work-for-hire agreement is yours completely. There's no licensing ambiguity, no GPL contamination risk, and no concerns about attribution requirements.

Freelance-written code can also cover niche domains and edge cases that are underrepresented in both open-source repositories and commercial datasets. If your model needs to handle domain-specific patterns — financial calculations, medical data processing, industrial control systems — freelancers with relevant expertise can produce examples that simply don't exist in public datasets.

Challenges

Scaling is slow and expensive. Each task requires writing a specification, finding qualified developers, reviewing submissions, providing feedback, and iterating. The time from specification to usable training data can be weeks or months, and costs per example are orders of magnitude higher than automated collection.

Quality depends heavily on individual contributors. Even experienced developers produce inconsistent output — varying coding styles, documentation quality, and attention to edge cases. A robust review and QA process is essential, which adds further time and cost. Some organizations use multi-stage review: automated linting and testing, followed by human review by a senior developer.

Managing a distributed team of freelancers also introduces coordination overhead. Clear specifications, style guides, and evaluation rubrics are necessary to ensure consistency across contributors.

Best For

Filling specific gaps in training data — rare languages, domain-specific patterns, evaluation benchmarks, and instruction-response pairs. Also valuable for creating high-quality fine-tuning datasets when the target domain is narrow and well-defined.

5. Dedicated In-House Coding Teams

The highest-investment option: building a team whose primary job is to produce high-quality training data. This means hiring developers who write code specifically for model training — creating examples, annotating code, writing paired natural language descriptions, and building structured prompt-solution datasets.

Advantages

This approach delivers the highest quality and consistency. In-house teams develop deep familiarity with your model's needs, coding standards, and quality criteria. Over time, they become increasingly efficient at producing exactly the kind of data that improves your model's performance.

You have full control over every aspect: coding style, complexity distribution, domain coverage, documentation depth, and language balance. This control is especially valuable when building models for specific verticals or use cases where generic training data falls short.

IP is completely clean — the organization owns all output with no licensing concerns. The team can also provide fast iteration: when the model struggles with a particular type of problem, the team can rapidly produce targeted training examples to address the weakness.

In-house teams also accumulate institutional knowledge about what makes training data effective. They learn which types of examples improve model performance, which patterns cause issues, and how to structure data for optimal learning. This expertise becomes a compounding advantage over time.

Challenges

This is by far the most expensive option. Developer salaries, management overhead, infrastructure, and tooling add up quickly. A team of 10-20 developers dedicated to training data production can cost millions of dollars per year.

Recruiting and retaining talent for this kind of work can be challenging. Writing training data is different from traditional software development — it requires a specific mindset that combines coding skill with an understanding of how models learn. Not every developer finds this work engaging long-term.

Scaling is difficult. Doubling output requires roughly doubling the team, with all the associated hiring, onboarding, and management costs. There are limits to how fast you can grow without sacrificing quality.

Best For

Organizations building flagship coding models where data quality is the primary competitive differentiator. Companies like Anthropic, OpenAI, and Google invest heavily in in-house data teams because they've found that data quality — not just model architecture — is the most important factor in model performance. If you're building a production coding assistant that needs to be best-in-class, this is where the investment goes.

How to Choose: Building a Blended Strategy

Most successful coding LLMs don't rely on a single data source — they use a carefully designed blend that optimizes for different factors at different stages of training.

The Typical Pipeline

• Pre-training (Volume): Start with large-scale open-source data. This provides the broad foundation of syntax, patterns, and language understanding. Focus on deduplication and basic quality filtering, but accept that some noise is acceptable at this stage.
• Continued Pre-training (Quality): Layer in curated and commercial datasets to improve code quality, introduce domain-specific patterns, and strengthen underrepresented languages or frameworks.
• Fine-tuning (Precision): Use targeted human-written code — from freelancers or in-house teams — for specific capabilities. This is where instruction-following, code explanation, debugging, and specialized domain knowledge get refined.
• Evaluation (Validation): Build evaluation sets exclusively from human-written, expert-reviewed code. Your benchmarks should reflect real-world coding patterns — otherwise you're optimizing for the wrong target.

Key Trade-offs

• Volume vs. Quality: More data isn't always better. After a certain point, adding low-quality data can actually degrade model performance. Focus on quality for fine-tuning stages.
• Cost vs. Control: Open-source data is free but noisy and legally complex. Commercial and human-written data costs more but gives you quality guarantees and licensing clarity.
• Speed vs. Licensing Clarity: Scraping open-source repos is fast but creates legal exposure. Properly licensed data takes longer to acquire but eliminates compliance risk.

At Pletava, we help teams design data pipelines that balance these factors based on their specific model goals, budget, and compliance requirements. The right blend depends on what you're building and who you're building it for.

Conclusion

There's no single "best" data source for training coding LLMs — the right choice depends on your model's purpose, budget, and risk tolerance. What matters most is being intentional about data curation, understanding the trade-offs of each source, and investing in preprocessing and quality assurance at every stage.

The organizations that treat training data as a strategic asset — not an afterthought — are the ones building the best models. Whether you're a startup working with limited resources or an enterprise with a dedicated AI team, the principles are the same: start broad, refine progressively, and never compromise on the quality of your fine-tuning and evaluation data.

The landscape of available code data continues to evolve — new datasets are released regularly, licensing frameworks are maturing, and the tooling for data curation is improving rapidly. Staying current with these developments and continuously refining your data pipeline is as important as improving your model architecture.