Large language models (LLMs) like ChatGPT, Claude, and Gemini are changing how businesses operate, from automating customer support to generating content and analyzing complex data. But as powerful as these tools are, they don’t manage themselves. That’s where LLMOps (large language model operations) come in.
What is LLMOps?
LLMOps is the process of managing an LLM throughout its entire lifecycle—from development and testing to model deployment, monitoring, and optimization. It includes rules, tools, and techniques for managing and optimizing the performance of an AI model in production environments.
This involves tuning, maintaining, and testing the model to ensure it performs consistently and stays aligned with business needs. In other words, LLMOps supports the infrastructure around your LLM-powered application to ensure it remains useful, safe, and reliable.
Why is LLMOps important?
LLMOps is important because large language models are powerful, but imperfect. Without the right processes and model monitoring in place, LLMs can:
- Provide wrong answers and data inaccuracies
- Leak sensitive info or risk security breaches
- Stop working when you change or update something in the software
LLMOps helps teams avoid these risks while ensuring a more consistent and high-performing model output. It supports ongoing llm evaluation and maintenance, helping teams get the most value from these models in real-world settings.
LLMOps vs. MLOps
Some may consider LLMOps and MLOps to be one in the same. However, LLMOps is a specialized subset of machine learning operations (MLOps).
MLOps is the overarching field that covers all types of ML models, whereas LLMOps focuses specifically on language-based generative AI models, such as GPT, Claude, and LLaMA.
Key differences between LLMOps and MLOps:
- Supported models: MLOps supports all ML models; LLMOps is purpose-built for language-based generative models.
- Focus: MLOps often involve training from scratch. LLMOps focuses more on prompt orchestration, fine-tuning, and inference.
- Data flow: LLMOps often requires managing unstructured text and context-aware inference, unlike the structured input/output typically found in traditional machine learning workflows.
Creating an LLMOps framework
Creating a scalable and efficient LLMOps framework is essential for organizations that rely on LLMs in production. A solid framework helps guide the entire llm pipeline—from ideation to implementation—so your team can deploy safely and effectively.
1. Define business objectives and use cases
Before implementing LLMOps, teams should ensure their AI strategy aligns with business goals and specific use cases. What should the AI model do, and how will it deliver value?
Implementation tips:
- Identify pain points and goals.
- Prioritize use cases based on business impact.
- Define KPIs to track LLM performance and demonstrate ROI.
2. Data collection and analysis
LLMs are trained using massive datasets, but their performance depends on the quality of the input. Teams must apply data science practices, such as exploratory data analysis (EDA), to collect, clean, and analyze data for relevance, accuracy, and compliance.
Implementation tips:
- Use automated pipelines for data ingestion and validation.
- Ensure compliance with data regulations (e.g., GDPR, CCPA).
3. Prompt engineering and testing
Prompt engineering plays a central role in shaping model output. It involves crafting instructions that help the LLM generate accurate, helpful responses.
Implementation tips:
- Use prompt chaining and templates to standardize queries.
- Test different prompt formats and evaluate results.
- Document successful strategies as part of your LLM pipeline.
4. Fine-tuning and customization
Fine-tuning an LLM helps adapt it to your domain-specific language or tasks. While not always necessary, it can boost performance for specialized use cases.
Implementation tips:
- Use transfer learning with pre-trained models.
- Leverage libraries like Hugging Face Transformers.
- Validate fine-tuned models with strong LLM evaluation practices.
5. Human feedback and reinforcement learning
Human-in-the-loop (HITL) feedback helps refine LLM behavior over time. Incorporating user insights improves accuracy and usability in your LLM application.
Implementation tips:
- Collect structured feedback from users.
- Apply reinforcement learning from human feedback (RLHF).
- Build continuous feedback loops into your model training process.
6. Governance, security, and compliance
Strong AI governance ensures the ethical and secure use of your LLM-powered application. As LLMs handle more business-critical tasks, oversight is key.
Implementation tips:
- Use role-based access controls and audit logs.
- Monitor for bias, hallucinations, and data drift.
- Regularly update governance policies to reflect evolving standards.
7. Monitoring, evaluation, and continuous improvement
Ongoing model monitoring helps detect performance issues and improve model accuracy. It’s critical for long-term success in any LLMOps workflow.
Implementation tips:
- Create dashboards to measure LLM performance.
- Use A/B testing and shadow deployments to evaluate changes.
- Schedule regular retraining to keep up with new data.
LLMOps best practices
As more organizations adopt generative AI, implementing LLMOps best practices helps ensure models are used responsibly, ethically, and effectively.
Do:
- Clearly define your use case and business objectives.
- Monitor model performance regularly through metrics and user testing.
- Ensure data quality and apply principles of data science.
- Use prompt templates and chaining to maintain consistency.
- Incorporate human feedback and iterate often.
- Track and version all changes in the LLM pipeline.
- Follow strong AI governance and compliance protocols.
- Protect user data with secure access controls and encryption.
Don’t:
- Rely on untested, generic prompts in a live LLM application.
- Assume pre-trained models are safe or accurate in all scenarios.
- Fine-tune without a clear business need or evaluation framework.
- Ignore cost tracking, especially with hosted APIs or frequent queries.
- Skip feedback loops or human review processes.
- Neglect security, governance, or access monitoring.
A well-defined LLMOps strategy is essential as LLMs become an integral part of modern workflows. From identifying business needs to optimizing model deployment and monitoring, LLMOps provides the structure needed to ensure your language models are safe, compliant, and effective.
By following best practices and avoiding common mistakes, your organization can confidently scale its LLM-powered applications and create real business value from machine learning innovation.
Learn how PagerDuty’s AIOps can work alongside LLMOps to help your team automate incident response, reduce downtime, and improve model reliability. Start your free trial today!