Training or evaluating a conversational or multimodal AI system is data-hungry. Most teams face two hard constraints: not enough high-quality labeled data, and real data that is noisy, biased, or too expensive to scale. Synthetic data can fix that, if you use it the right way.
The data bottleneck is real
Modern LLMs and vision-language models need billions of tokens. Even for specialized tasks, a single high-quality dataset can cost thousands of dollars and months of labeling. A 2023 benchmark found that teams using manually labeled datasets for multimodal tasks saw 20-40 percent higher variance in evaluation scores compared to teams that invested in curated, diverse training sets. In other words, quantity alone isn’t enough, quality and diversity drive robust performance.
What synthetic data actually gives you
- Control over format and labels: you define the schema, task, and constraints.
- Unlimited scale: generate millions of examples at near-zero marginal cost.
- Safety and compliance: avoid PII, copyrighted content, or sensitive domains.
- Coverage of edge cases: design rare scenarios that rarely appear in real data.
Training vs evaluation: use cases differ
Synthetic data shines in both training and evaluation, but the approach varies. For training, you want diversity, coverage, and alignment with your downstream metrics. For evaluation, you need realistic, hard cases that reveal failure modes. A recent study showed that synthetic test sets can uncover 30-50 percent more edge cases than manual QA, leading to faster iteration cycles. However, if synthetic training data is too far from reality, models may learn spurious patterns that don’t transfer to production.
The key is to match the synthetic data generation method to your goal: varied, high-fidelity data for training; realistic, adversarial scenarios for evaluation.
Techniques that actually work
- Prompt-based generation for conversational data: use strong prompts and meta-prompts to produce dialogues that match your domain and tone.
- Rule-based or constraint-guided generation for multimodal data: enforce layout rules, formatting, and visual consistency.
- Iterative refinement: compare synthetic outputs to real examples, then update your generation pipeline.
- Hybrid approaches: combine synthetic data with a small amount of high-quality real data to balance coverage and realism.
How Coasty fits
Coasty runs computer-use agents on real desktops and browsers, capturing realistic interaction data and trajectories. This lets teams create custom synthetic datasets that reflect how humans actually use software. Because Coasty’s offering is custom and contact-led, you work directly with the team to design datasets that match your product’s specific needs and evaluation criteria. No self-serve dashboard, no fixed packages, just a tailored solution built around your requirements.
If you're building or evaluating conversational or multimodal AI, synthetic data can close the gap between limited real data and the performance you need. To see how Coasty can help you build a custom synthetic dataset for your use case, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.
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