Tutorial•15 min read
Complete Guide to Building RAG Applications
Learn how to build production-ready RAG applications with comprehensive implementation examples and best practices.
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Everything you need to train, fine-tune, deploy, and monitor your custom LLMs in one unified platform.
Train custom LLMs from scratch with an intuitive interface. Support for distributed training across multiple GPUs and TPUs.
Fine-tune pre-trained models on your datasets with advanced hyperparameter optimization and automated experiment tracking.
Deploy trained models to production with auto-scaling, load balancing, and zero-downtime updates.
Upload, version, and manage training datasets with built-in data validation, preprocessing, and augmentation tools.
Monitor model performance, track inference metrics, and optimize costs with comprehensive analytics dashboards.
Unified REST and GraphQL APIs with automatic versioning, rate limiting, and usage-based billing.
SOC2 compliant infrastructure with end-to-end encryption, VPC deployments, and role-based access control.
Track model versions, rollback to previous releases, and A/B test different models in production environments.
From training to deployment - get your custom LLM in production in three simple steps.
Upload your training datasets to our platform. Use built-in tools for data preprocessing, validation, and augmentation.
Configure training parameters and start training. Monitor progress in real-time with experiment tracking and hyperparameter optimization.
Deploy with one click to production endpoints. Monitor performance, track usage, and optimize costs through our dashboard.
Train and deploy custom LLM models tailored to your specific business needs and use cases.
See how teams are using Neuralyx AI to train and deploy custom LLM models faster.
We trained a custom chatbot for our support team using Neuralyx AI. The platform's intuitive interface made it easy to fine-tune on our knowledge base. Deployment took minutes, not weeks.
Sarah Chen
CTO, FinTech Solutions Inc.
The training dashboard gives us real-time insights into model performance. We can A/B test different versions and rollback if needed. This level of control is exactly what we needed.
Michael Rodriguez
VP of Engineering, DataCorp
Fine-tuning our domain-specific model on Neuralyx improved accuracy by 45%. The automated hyperparameter optimization saved us weeks of manual experimentation.
Amanda Foster
Head of AI, TechGlobal Enterprise
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Access our suite of free tools to help you estimate costs, benchmark performance, and optimize your LLM applications.
Calculate and compare costs across different LLM providers and models. Get accurate pricing estimates for your specific usage patterns.
Try CalculatorBenchmark your RAG system performance with various metrics. Upload datasets and get detailed analysis reports.
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Learn how to build production-ready RAG applications with comprehensive implementation examples and best practices.
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