Coding Datasets for LLM Fine-Tuning and Code Generation
Expert-written code generation pairs, bug fix examples, and code explanation datasets. Built for fine-tuning code LLMs on proprietary languages, frameworks, and internal coding standards.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond Public Code Benchmarks
HumanEval and MBPP benchmark general Python code generation. Fine-tuning code LLMs for proprietary codebases, internal APIs, or domain-specific languages requires expert-written examples those benchmarks cannot provide.
Enterprise code assistants must understand internal libraries, custom frameworks, and company-specific coding standards. General code models produce syntactically correct but contextually wrong code when applied to proprietary systems.
Off-the-shelf coding datasets suffer from language bias (Python-heavy; limited coverage of TypeScript, Rust, Go, or proprietary DSLs) and framework gaps for internal or niche libraries.
LXT builds custom coding datasets tailored to your programming languages, frameworks, and coding standards. We deliver instruction-code pairs, bug fix examples, and code explanation data that teach your LLM the idiomatic patterns of your codebase.
Why Teams Upgrade
Limitations of Public Code LLM Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| HumanEval | 164 Python problems only; simple algorithmic tasks; no framework usage, API integration, or multi-file context | Python-only |
| MBPP | 500 Python beginner problems; very limited scope; no real-world code patterns, dependencies, or software engineering tasks | Toy scale |
| CodeContests | Competitive programming focus; unusual algorithmic patterns not found in production code; no engineering task coverage | Competition-style |
| DS-1000 | Data science only; 7 libraries; no backend, frontend, systems, or language-diverse engineering coverage | DS-only |
| StarCoder Data | Web-scraped GitHub code; no instruction-response format; no quality filtering for correctness or style | No instructions |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Language Coverage
Programming Languages
- Target Languages: Python, TypeScript, Go, Rust, Java, C++, Kotlin, or custom DSLs
- Frameworks: Internal APIs, open-source frameworks, and proprietary library bindings
- Paradigms: OOP, functional, async, event-driven, and systems programming patterns
Task Types
Code Generation Coverage
- Generation: Natural language instruction to working code implementation
- Bug Fix: Buggy code snippet with corrected version and error explanation
- Explanation: Code block with natural language explanation for documentation training
Quality Controls
Expert Standards
- Execution Tests: Generated code verified by running unit tests or static analysis
- Code Review: Senior engineers review code style, efficiency, and correctness
- Standards Compliance: Output conforms to your language and framework style guides
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Edge Cases in Code Datasets
High-accuracy models handle rare attributes that public datasets miss.
Multi-File and Module-Level Tasks
Real engineering tasks span multiple files and require understanding of module interfaces. Annotated multi-file context examples train models to generate code that integrates with existing systems.
Framework-Specific Patterns
Internal frameworks and library APIs have idiomatic usage patterns. Expert engineers who know your specific stack write examples that teach correct framework usage rather than generic code.
Security-Sensitive Code Patterns
Authentication, cryptography, and input validation code requires expert review to ensure training data does not contain insecure patterns. Security-reviewed code examples are available.
Deprecated and Version-Specific APIs
Codebases span multiple framework versions. Version-tagged examples and migration pair annotations train models to understand version-specific usage patterns.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
Expert Code Writing
Senior engineers write instruction-code pairs following your language and style standards. All code executed and tested before inclusion in the dataset.
Bug Fix Annotation
Real or synthetic bugs introduced into correct code with expert-written corrections and natural language error explanations for debugging assistant training.
Code Explanation Writing
Technical writers and engineers produce accurate natural language explanations of code blocks for code documentation and explanation model training.
Industry Applications
Coding Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Code Assistants
Autocomplete, generation, and inline suggestions
Debugging AI
Bug detection, error explanation, fix suggestion
Documentation AI
Docstring generation, API documentation, code comments
Code Review AI
Style enforcement, best practice suggestions
Migration AI
Framework upgrades, language migration, refactoring
Test Generation
Unit test and test case generation from code
DevOps AI
Infrastructure-as-code, CI/CD script generation
Low-Resource Languages
Custom language model for niche or proprietary languages
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
Coding Dataset FAQs
Can you write examples for our internal framework?+
Yes. We onboard senior engineers to your internal framework documentation and codebase. They write idiomatic examples that match your coding standards and API patterns.
How do you verify code correctness?+
All generated code is executed against unit tests or verified by static analysis. For domain-specific code that cannot be executed, senior engineers perform manual correctness review.
What programming languages can you cover?+
We cover all major languages (Python, TypeScript, Go, Rust, Java, C++, Kotlin, Swift) and can onboard to proprietary DSLs or domain-specific languages with documentation access.
Can you annotate our existing codebase for fine-tuning?+
Yes. We produce instruction-code pairs from your existing codebase, creating natural language descriptions for code functions and modules. Requires access to your codebase under a data agreement.
How many examples do I need to fine-tune a code model?+
For adapting a general code model to a specific framework or language, 5,000-50,000 high-quality examples typically produce measurable improvement. Volume recommendations depend on your base model and task specificity.
What does a custom coding dataset cost?+
Projects range from $20K for focused single-language datasets (1,000-5,000 pairs) to $150K+ for large multi-language, multi-task datasets with execution-verified code.
Can you produce security-reviewed code examples?+
Yes. Security review by engineers with application security expertise is available as an add-on for authentication, cryptography, and input handling code examples.
Get Started
Scope Your Custom Coding Dataset
Share your target languages, frameworks, and task types. A code AI data specialist will provide a detailed proposal within 48 hours.
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