LLM development for enterprise is not “call an API and ship.” It is domain data, evaluation harnesses, retrieval architecture, cost control, and change management.
The enterprise LLM stack
- Use-case definition and risk classification
- Data curation and privacy controls
- Retrieval (RAG) and/or fine-tuning
- Evaluation against business metrics
- Serving, caching, fallbacks, and monitoring
- Human oversight where stakes are high
RAG vs fine-tuning
RAG connects models to your knowledge base and is usually the fastest path to accurate, citable answers. Fine-tuning helps when you need domain style, vocabulary, or task format consistently. Many production systems use both.
Evaluation that actually matters
Demos hide failure. Production LLM evaluation needs representative real-world tests, automated checks tied to business outcomes, and human review on high-impact outputs.
Cost and latency
Token costs and non-deterministic latency force platform thinking: prompt management, caching, model routing, and budget alerts — not ad-hoc calls buried in app code.
Work with 1touch.ai
Our LLM development practice builds production language systems for document analysis, support, research, and operations. Contact us for a scoped assessment.