By Glenn Donghi
Large Language Models (LLMs) are increasingly embedded in business applications such as chatbots, writing assistants, and document-based Q&A systems. These use cases are easy to prototype and often deliver impressive early results. The real challenge begins when they move into production.
This is where LLM Ops becomes essential, not as a purely technical layer, but as a way to maintain quality, control, and trust at scale.
A simple use case and a critical question
Imagine a journalist writing an article in a content management system. With a single click, the system generates ten headline suggestions using an LLM.
The initial setup is straightforward:
- The article body is passed as input
- A prompt instructs the model to generate ten headlines
- The output is returned directly to the user
At first, this works. Now pause for a moment and ask the same question raised in the presentation: What could go wrong if this goes live as-is?
When naive implementations meet reality
In practice, several issues surface quickly.
Sometimes the system returns five headlines instead of ten. Sometimes the titles are too long, inconsistent in tone, or misaligned with editorial expectations. When this happens, journalists spend time correcting outputs, or stop using the tool altogether.
More critically, when a questionable headline appears in production, there is no clear way to explain why it happened. Which prompt was used? Which model configuration? What input triggered the result? Without visibility, accountability becomes impossible.
And as prompts are adjusted to satisfy one group of users; say, sports journalists seeking more punchy titles, others may see quality decline. Complaints surface informally, but there are no metrics to confirm or compare changes.
These are not model failures. They are operational failures.
How LLM Ops addresses these problems
Each of these issues maps directly to a core LLM Ops capability.
- Inconsistent outputs are addressed through structured response schemas and clearer prompt instructions, ensuring predictable and valid results.
- Lack of visibility is solved through tracing, where every LLM call logs inputs, outputs, prompt versions, model configurations, and parameters.
- Uncontrolled changes are mitigated with prompt versioning, allowing teams to track, compare, and roll back prompts across environments.
- Subjective feedback is replaced by evaluation sets, where prompts are tested against representative inputs before being released.
- Lost user feedback is captured and linked directly to individual executions, rather than scattered across emails or conversations.
Together, these practices turn experimentation into a controlled system.
LLM Ops is not traditional MLOps
Unlike traditional MLOps, LLM-based systems typically rely on pre-trained foundation models rather than frequent retraining. The operational focus shifts from model accuracy to managing prompts, qualitative evaluation, hallucination risk, cost, latency, and reliability.
The value lies in controlling how the model is used, not in rebuilding the model itself.
A final reflection
Before putting an LLM-powered feature into production, consider:
- Can you see which prompt was used for a result from last week?
- Can you trace an output back to its exact inputs and configuration?
- Do you evaluate prompt changes before releasing them?
- Is user feedback linked to specific executions?
If the answer to these questions is unclear, the issue is not the LLM. It’s the lack of LLM Ops.
If you’re exploring how to bring LLM Ops into your organization, we’re always happy to discuss what these foundations could look like in your context, reach out to us 👉https://go.talan.com/l/688263/2024-11-11/2cjpst?utm_source=dataroots&utm_campaign=forms