Fuel Smarter Coding LLMs with Precisely Labeled Data!
We offer data labeling services for coding models to create supervised fine-tuning datasets by analyzing, annotating, and refining code snippets, dialogues, and programming tasks. This ensures accuracy, coherence, and optimal performance in coding-focused LLMs.
Trusted by Industry Leaders Worldwide
Our Capabilities
Deliver precise code annotations and build high-quality SFT datasets tailored for training and fine-tuning coding LLMs.
Supervised Fine-Tuning (SFT)
Human Preference Ranking (RLHF)
LLM Evaluation & A/B Testing
LLM Red Teaming
- Prompt generation.
- Prompt verification.
- Answer generation.
- Answer verification.
- Dialogue generation.
- Dialogue evaluation.
- Bug detection and fix suggestions.
- Real-time human interactions.
- Evaluation of single- or multi-turn conversations.
- Customizable evaluation criteria: semantic accuracy, syntax compliance, performance optimization, and more.
- Detailed comparisons between code generation models.
- Evaluation based on correctness, performance, and coherence.
- Support for both qualitative and quantitative analysis of model responses in specific programming scenarios.
- Insecure code generation.
- Malicious or inappropriate suggestions (e.g., bypassing authentication, SQL injection).
- Multi-turn testing using real-world scenarios.
Our Data Labeling for Coding LLMs Workflow
Follow our expert-driven process to solve coding tasks at scale.
We begin by setting up the project team, including both internal and vendor teams, and then assign tasks based on the required programming languages. We conduct training sessions for both our delivery team and vendors to clarify guidelines and answer questions. Finally, we hold meetings with both teams to align on the execution methodology.
We carry out trial tasks and deliver them to the client. After receiving feedback, we organize follow-up meetings with internal and external delivery teams. Based on the results and feedback, we update the guidelines to address new scenarios or edge cases identified during this phase.
- If a batch achieves a ≥90% acceptance rate, the entire batch is approved.
- If a batch has a ≥90% rejection rate, the entire batch must be reworked and resubmitted.
We report externally caused rejections (unclear descriptions, hidden requirements) to the client for clarification. Additionally, we meet every other day to address and resolve internal errors discovered during the execution process.
Why LTS GDS?
Trust our SFT and RLHF process to accelerate coding LLM development.
Superior Quality
Rigorous QA processes are implemented to build precise Supervised Fine-tuning (SFT) datasets with up to 99% accuracy, specifically designed for training high-performing coding models.
Proven Expertise
100+ seasoned developers mastering in SQL, Python, C#, JavaScript, TypeScript, Bash, .NET, Scala work tirelessly to ensure LLMs generate code fast, logical and bug-free.
Quick Team Ramp-up
LTS GDS guarantees to build up a dedicated team consisting of a battle-hardened PM and up to 200 man-months from in-house team and our partner network for large-scale projects within 2 weeks.
Cost-effectiveness
Global businesses can get IT experts to adapt pre-trained models to coding-specific LLMs with optimal budgets in light of the expense gaps of Vietnam outsourcing market and favorable tax policies.
Wall of Achievement
99%
Accuracy
10M+
Lines of Code
11
Countries
200+
Projects
Our Case Studies
Explore real-world examples of how our data labeling services have turbocharged more accurate coding LLMs.
Our Tools and Technologies
Leverage advanced tools and custom-built systems to streamline annotation for coding and quality control.
FAQs about Fine-tuning LLMs for Coding and Programming
What is fine-tuning for LLMs in coding?
Fine-tuning is the process of taking a pre-trained large language model and training it further on a curated dataset of source code or code-related tasks. This allows the model to specialize in programming-specific functions such as code generation, debugging, or documentation, etc.
What is RLHF?
Reinforcement Learning from Human Feedback (RLHF) is a method used to improve LLMs by incorporating human preferences. Following initial training, human feedback is integrated into this process to further train LLMs for better response performance.
What is the difference between SFT and RLHF?
Supervised Fine-tuning (SFT) involves training LLMs using labeled data to teach task-specific behavior. RLHF then follows, utilizing human feedback and reinforcement learning to refine outputs and align them with human values. SFT teaches what to say, while RLHF refines how to say it.
How does fine-tuning differ from prompt engineering?
Fine-tuning uses specific datasets to adjust an LLM’s parameters for specialized coding tasks. In contrast, prompt engineering focuses on crafting better input prompts to guide the model’s responses, without changing the model itself.
What types of coding tasks can fine-tuned coding LLMs perform?
Fine-tuned LLMs can generate code, provide answers, create dialogues, and evaluate logic. They can also translate between languages, generate documentation, and assist with DevOps scripts. When trained on specific codebases, they master domain-specific development tasks.
What are the benefits of fine-tuning a code-specific LLM?
Fine-tuned coding LLMs offer improved accuracy, fewer errors, and a better understanding of specific programming languages or codebases. They provide more relevant suggestions, support niche frameworks, and can be customized to align with internal coding standards.
Awards & Certifications
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