The most experienced foundation model training company
Train LLMs to code smarter and build faster
Enhance your LLMs’ coding capabilities with human-generated training and evaluation datasets for improved code generation, debugging, and test case creation.
Human expertise meets AI-powered coding
Turing combines advanced AI technology with human expertise to train LLMs for coding excellence. Our team of professionals designs and evaluates datasets to improve code generation, error resolution, and adherence to best development practices. Leverage our expertise to ensure your LLMs deliver precise, secure, and efficient coding outputs tailored to your needs.
LLM coding specialties
Language-specific training
Task-oriented code generation
Debugging and error resolution
Secure and ethical code outputs
Real-time context-aware support
Synthetic data and performance optimization
Advanced LLM coding starts here
Ready to build smarter LLMs for coding excellence?
Task evaluation and scoping
Our experts work with you to define your coding requirements, evaluate current gaps, and set clear training objectives tailored to your business needs.
Team assembly and dataset creation
We assemble a team of coding experts and data scientists to design high-quality, language-specific datasets. Use a human-in-the-loop approach to ensure accuracy, relevance, and task-specific coverage.
Training execution and fine-tuning
Train and fine-tune your LLMs with advanced techniques, focusing on secure, efficient, and task-oriented coding capabilities. Incorporate real-world debugging scenarios and edge-case handling.
Scale on demand
Expand your AI’s coding expertise as your business grows. Scale training efforts to integrate new languages, frameworks, or domain-specific requirements seamlessly.
Ready to build smarter LLMs for coding excellence?
Talk to our solutions architects and explore how Turing’s coding training solutions can transform your AI-powered software development.
Cost-efficient R&D for LLM training and development
Empower your research teams without sacrificing your budget or business goals. Get our starter guide on strategic use, development of minimum viable models, and prompt engineering for a variety of applications.
“Turing’s ability to rapidly scale up global technical talent to help produce the training data for our LLMs has been impressive. Their operational expertise allowed us to see consistent model improvement, even with all of the bespoke data collection needs we have.”
Want to revolutionize your development workflow?
Talk to our solutions architects and explore how Turing’s coding training solutions can transform your AI-powered software development.
Frequently asked questions
Find answers to common questions about LLM coding training and explore how it enhances code generation, debugging, and performance for smarter, efficient AI solutions.
What coding tasks can LLMs handle after training?
LLMs can perform code generation, completion, debugging, inline suggestions, test case creation, and competitive programming solutions.
How does Turing ensure high-quality coding datasets?
Our team of coding experts designs datasets tailored to specific languages and tasks, validated through a human-in-the-loop process for accuracy and relevance.
Can LLMs trained by Turing generate secure code?
Yes, we train models to identify vulnerabilities, handle adversarial prompts, and produce sanitized, secure code to prevent security risks.
How does Turing handle debugging tasks in LLMs?
We train models on annotated datasets of common errors, including step-by-step solutions, to improve error resolution and debugging capabilities.
What programming languages are supported?
We specialize in Python, Java, C++, SQL, Verilog, Haskell, and more, ensuring your models excel across multiple languages.
Can LLMs integrate enterprise coding practices?
Yes, we use RAG methods to incorporate internal repositories, coding styles, and best practices into the models for real-time, context-aware support.
What are the benefits of synthetic data for training LLMs?
Synthetic data covers edge cases and complex scenarios, ensuring models are robust, adaptable, and capable of handling diverse coding tasks.