“I’m using LLMs in my project and I can trust that they will do a great job. I don’t need any annotation rounds.”
Some teams think that they don’t need any annotations because they’re just using LLMs in their projects and they can trust them to do a good job, but this is a misleading thought. Even if you think OpenAI models will do a great job at generating an answer for your users, at some point if you really want a production-ready product, you will need some annotated data for different stages in your project.
The Illusion of “Good Enough”
When you’re testing an LLM in development, you’re usually looking at cherry-picked examples or small-scale interactions. The model impresses you with its reasoning, creativity, and general capabilities. This creates a dangerous feedback loop: you see mostly success cases, so you assume the success rate is higher than it actually is.
But production is different:
- Edge cases are more common than you think
- Failure modes compound across your system
- Users find every possible way to break your assumptions
- “Usually correct” means “regularly fails at scale”
Without systematic annotation, you have no reliable way to measure how often your LLM is actually meeting your requirements.
Where You Actually Need Annotated Data
Let’s walk through a typical LLM project lifecycle and see where annotation becomes critical:
1. Evaluation and Benchmarking
You can’t improve what you don’t measure. How do you know if your prompt changes actually improved output quality? How do you compare GPT-4 vs Claude vs Gemini for your specific use case?
You need a gold-standard evaluation set—examples with human-annotated “correct” outputs or quality ratings. Without this, you’re flying blind, making changes based on vibes.
2. Fine-tuning (Yes, Even for RAG Systems)
Think you can skip fine-tuning? Maybe. But if you’re serious about performance, cost, or latency, you’ll eventually want a smaller, specialized model. And fine-tuning requires annotated training data—lots of it.
Even if you’re “just doing RAG,” you might fine-tune for:
- Better query reformulation
- More accurate relevance judgments
- Improved citation generation
- Domain-specific reasoning patterns or professional standards
3. Guardrails and Safety
LLMs hallucinate. They generate biased outputs. They occasionally produce completely nonsensical responses. If you’re building a production system, you need:
- Annotated examples of hallucinations in your domain
- Safety violation datasets specific to your use case
- Quality boundaries that define “acceptable” vs “unacceptable” outputs
Generic safety datasets won’t cut it. Your users, your domain, and your failure modes are unique.
4. Monitoring and Drift Detection
Models change. APIs get updated. User behavior evolves. How do you know if your system’s quality is degrading over time?
You need ongoing annotation—either of random samples or of edge cases flagged by your system—to monitor for:
- Quality drift after model updates
- Emerging failure patterns
- Gaps in your coverage as user needs evolve
The Hidden Costs of Skipping Annotation
“But annotation is expensive!” Yes. So is:
- Lost user trust when your system confidently presents incorrect information
- Support tickets from confused users dealing with edge cases
- Engineering time debugging problems you can’t reproduce because you have no systematic testing
- Opportunity cost of not knowing which improvements would actually move the needle
You could spend weeks optimizing prompts and switching between models, only to discover through proper evaluation that the “improvements” made things worse. Annotation isn’t just an expense—it’s how you avoid wasting everything else.
Start Small
You don’t need 10,000 annotated examples on day one. Start pragmatically:
- Create a small eval set (50-100 examples) covering your core use cases and known failure modes
- Annotate systematically as you find bugs—turn each production issue into a test case
- Build a continuous annotation pipeline for random sampling of production outputs
- Involve domain experts early, especially for specialized fields where LLM errors are subtle
- Automate what you can if there’s anything that can be done with a testing function instead of annotations,
The goal isn’t perfection. It’s visibility. You need to see what’s actually happening with your system, not what you hope is happening.
The Real Question
The question isn’t “Do I need annotation?” It’s “How much annotation do I need and what parts of my pipeline should I cover?”
If you’re building a weekend project or exploring ideas, sure, skip it. But if you’re building something users will depend on, something you’ll maintain over time, something that needs to actually work—annotation isn’t optional.
Your LLM is powerful. It’s not magic. And the difference between a impressive demo and a reliable product is systematic evaluation, which requires ground truth data.