Functional Usage
This page provides concise answers to common questions about using LLMOps.
General
Q1: What is LLMOps?
A1: LLMOps is an enterprise platform for managing the lifecycle of large language models (LLMs), including customization, deployment, monitoring, and application development.
Q2: What are the main features of LLMOps?
A2: Core features include Model Fine-Tuning, Model Compression, App Design (no-code/low-code), Monitoring, Validation Flows, and an App Store with pre-built applications.
App Design
Q3: How do I create a new AI application?
A3: Use the App Design feature. Open App Design, click "New Flow," then use the visual drag-and-drop canvas to assemble blocks, configure logic, and connect model components.
Q4: What types of applications can I build with App Design?
A4: Common applications include chatbots, content generators, data analysis tools, automated workflows, and custom integrations for business processes.
Q5: Is coding required to use App Design?
A5: No. App Design supports no-code and low-code workflows; custom code can be added where advanced behavior is needed.
Models & Fine-Tuning
Q6: Can I fine-tune existing models?
A6: Yes. Select a base model, upload or point to your dataset, configure training parameters, and run the fine-tuning job.
Q7: Which models are supported for fine-tuning?
A7: LLMOps supports various open-source models such as LLaMA, Mistral, Phi, Gemma, and other commonly used checkpoints (subject to licensing).
Q8: How can I track the performance of fine-tuned models?
A8: Use the Monitoring Dashboard to view metrics like latency, token usage, throughput, and custom evaluation metrics defined in validation flows.
Model Compression & Inference
Q9: What is Model Compression and how does it work?
A9: Model Compression reduces model size and inference cost with techniques such as quantization and pruning while aiming to preserve performance. Options vary by target hardware and accuracy requirements.
Q10: What compute options are available for training and inference?
A10: LLMOps supports multiple cloud providers (AWS, GCP, Azure) and a range of GPU configurations to match model sizes and workload needs.
Monitoring, Validation & Governance
Q11: How does LLMOps help with monitoring LLM usage?
A11: Monitoring captures API calls, token usage, request trends, latency, and error rates. Dashboards and alerts help identify anomalies and regressions.
Q12: How does LLMOps ensure output quality and safety?
A12: Validation Flows let you define checks such as bias detection, gibberish detection, toxicity filters, and business-rule validations. These can be applied to test and production traffic.
Q13: Is LLMOps suitable for handling sensitive data?
A13: Yes. The platform provides data governance features, PII detection, and configurable validation to help meet compliance requirements. Follow your organization’s data-handling policies.
Integration & Operations
Q14: Can LLMOps be integrated with existing systems?
A14: Yes. LLMOps provides APIs and connectors for common enterprise systems. For bespoke integrations, consult the integration guide or contact support.
Q15: Is there a limit to the number of projects I can create?
A15: Limits depend on your licensing and subscription. Check your account plan or contact sales for specifics.
Support & Updates
Q16: How often is LLMOps updated?
A16: LLMOps is regularly updated. Check release notes or the product changelog for details on new features and fixes.
Q17: Where can I get help or request integrations?
A17: For support, integration assistance, or enterprise requirements, contact the support or sales teams through the official channels defined in your account.