LLM Development Services
LLM development covers the technical work of getting a large language model to perform reliably for a specific business task — prompt design, choosing between RAG and fine-tuning, and evaluating output quality.
Benefits
- Systematic evaluation instead of guesswork on model choice
- Prompts and system design tuned to your specific task
- Clear reasoning behind fine-tuning vs retrieval decisions
Problems It Solves
- Inconsistent AI output quality across similar requests
- Uncertainty about whether fine-tuning is worth the cost
- No structured way to test and compare model performance
Who It's For
- Technical teams
- Software product teams
- Businesses building AI-native features
Common Use Cases
- Evaluating and selecting the right model for a task
- Building an evaluation harness for output quality
- Deciding between prompt engineering, RAG and fine-tuning
How We Deliver It
- 1
Task definition
We define what 'good output' means for the task, with examples.
- 2
Build and evaluate
We build the approach and test it against an evaluation set.
- 3
Iterate
We refine based on evaluation results before production rollout.
Technologies
- OpenAI API
- Anthropic Claude
- Evaluation frameworks
FAQs
Tell us what you're trying to solve.
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