The Emerging Field of LLM Ops

What is LLM Ops and Why Does It Matter?

Companies are rushing to build LLM-powered apps.

But without LLM Ops, you're flying blind.

Evaluating the quality of LLM outputs is uniquely complex.

LLM outputs are probabilistic.

The same input does not guarantee the same output, complicating debugging.

LLM outputs can be wrong, unhelpful, poorly formatted, or even hallucinated.

In traditional software development, you deal with structured data.

But LLMs deal with unstructured language.

For example, you want JSON output.

But there’s a small chance the LLM will occasionally return an invalid JSON, something un-parseable. This could hurt the user experience or even crash your system, if lacking robust error handling.

APIs, like OpenAI, are easy to plug in.

But you have zero visibility into what’s going on, how an input was processed.

Furthermore, the open-ended nature of user prompts adds ambiguity.

User prompts are often unstructured, which can lead to unpredictable outputs due to slightly varying:

  • word reordering

  • capitalization

  • punctuation

  • phrasing

Managing the computational costs of LLMs is also critical - it adds up fast!

And real-time use cases (chatbots) require low latency.

LLM Operations (“LLM Ops”) tackles these challenges.

What is LLM Ops?

LLM Ops is all about making LLMs work in production.

This involves:

  • LLM comparison and selection

  • data management

  • prompt engineering

  • deployment to production

  • latency and scalability

  • continuous monitoring and QA

  • session debugging

  • handling edge cases

  • continuous finetuning

  • implementing guardrails

  • bias and risk reduction

  • sensitive data/IP protection

  • 3rd party integrations

  • cost optimization

  • managing agents

It’s an emerging space, arguably the next version of the traditional ML Ops role.

All companies seriously building with LLMs will need to embrace LLM Ops.

Why LLM Ops?

There are numerous benefits I’ll dive into below.

LLM Ops positively transforms:

  • User Experience

  • Compliance and Safety

  • Development and Debugging

  • Quality Assurance

  • Cost Management

  • Latency and Performance

User Experience


Monitoring LLM sessions uncovers deep insights about your users.

By observing how users talk with LLMs, such as a customer support chatbot, you can proactively address issues and understand sentiment.

Analyzing data from LLM sessions can further help you hyper-personalize apps to specific user personas.

Human Feedback

LLM tasks are often open-ended. This makes human feedback important in evaluating LLM performance. Dedicated LLM Ops tools can also provide a unified way to gather labeled data from human feedback.

Integrating human feedback loop in your LLM Ops stack simplifies evaluation, helps detect errors and edge cases, and provides data for finetuning.

This feedback loop improves the user experience over time.

Compliance and Safety

Regulatory compliance is particularly vital in areas like healthcare and finance.

LLM Ops involves continuous logging, monitoring, and guardrails to ensure compliance with legal standards, minimizing risk of bias and harmful outputs.

As LLMs often process large volumes of potentially sensitive data, managing data privacy and security is paramount.

LLM Ops helps reduce risk of data breaches and unauthorized access.

It also plans for potential vulnerabilities such as adversarial attacks. Malicious prompt injections or deceptive prompts may to lead to bias or harmful outputs.

Development and Debugging

When it comes to building, debugging, and iterating on LLM-powered apps, LLM Ops plays a key role in multiple ways:

Regression testing

It’s important to mitigate the risk of LLMs disrupting user flows.

Some examples:

  • output not parsed correctly

  • incorrect summary produced

  • wrong article retrieved in RAG settings

  • OpenAI API call failed or produced bad output

Before implementing changes in production, LLM Ops tools allow you to run evaluations and test sets to predict how changes will impact performance.

This ensures only positive changes are introduced, safeguarding the user experience against degradation.

LLM regression testing should be an integral part of your CI/CD pipeline.


Many LLM Ops platforms offer streamlined debugging UIs to easily inspect and troubleshoot LLM traces and sessions, pinpointing precisely where, when, and why issues occur. This screenshot is Langfuse’s Trace Detail UI — it’s easy to see the input, output, metric scores (e..g conciseness), latency, and cost.

Version Tracking

Just like code versioning with git, versioning prompts and regression test sets is important in iterative development. This allows you to rapidly iterate and experiment with prompt variants, knowing you can always fall back to a reliable version if needed.

Quality Assurance

By implementing continuous monitoring, LLM Ops confirms that outputs remain consistent with the input context and user intent.

This helps maintain high relevance and avoids LLM model drift.

For example, when OpenAI releases a new version or you deploy an internal update, you’ll want to be notified immediately if output quality declines.

LangKit is an example LLM Ops tool that can identify when outputs deviate from expected behaviors or rules, such as producing toxic or off-topic content.

Just like finding bugs in a non-AI software app, immediately surfacing LLM output anomalies is essential to a smoothly running QA pipeline.

Many LLM Ops tools allow you to define quality metrics and measure them via:

  • human feedback

  • LLM-driven scoring

  • custom computed metrics

  • comparison to human-scored examples

Then, you can evaluate these quality metrics in regression tests, comparing prompt versions, and across different LLMs.

Cost Management

LLMs are expensive.

As enterprise AI adoption skyrockets, I anticipate LLM costs will become as significant as cloud provider costs.

We’re talking millions of dollars per month!

LLM Ops manages and optimizes costs by analyzing usage and identifying inefficiencies.

By tracking token usage, you can observe spikes in costs and monitor which services are consuming the most resources, then optimize further.

This free OpenAI pricing calculator helps you model cost projections:

Latency and Performance

Monitoring latency is vital for real-time use cases like chatbots.

LLM Ops tools can measure and segment latency by user, session, location, model, and prompt.

Unfortunately with turnkey APIs, you’re bound by tokens per second as well as the API’s underlying infrastructure latencies.

However, if you have a complex LLM pipeline, traces are incredibly useful to see where bottlenecks lie.

And if you’ve deployed hundreds or thousands of LLMs, LLM Ops serves as an orchestration tool. You can control, update, deploy, and monitor them all simultaneously. Plus, these operations can be repeated reliably, rather than via manual tasks, thereby accelerating your iteration cycles.


Companies are rapidly developing LLM-powered apps, recognizing their potential to transform and automate key workflows.

But without LLM Ops, managing these apps is challenging.

LLM Ops ensures apps perform reliably, remain user-friendly, and comply with legal and ethical standards.

It involves selecting the right LLMs, managing data, prompt engineering, deploying to production, and continuously monitoring and finetuning.

LLM Ops streamlines development, reduces risks, protects data, and optimizes costs, ultimately enhancing the quality of LLM apps.

As AI usage grows, embracing LLM Ops is key for companies to build great user experiences, maintain a competitive edge, and innovate efficiently.