The gap between an LLM demo and an LLM feature in production is roughly twelve months of unglamorous engineering. Eval harnesses. Cost controls. Latency budgets. Hallucination guards. Red-team exercises. The teams shipping AI that customers actually trust are the ones treating it as a software engineering problem, not a model selection problem.
Retrieval before fine-tuning
Most "the model doesn't know our data" problems are retrieval problems. Build a serious RAG pipeline — hybrid search, reranking, structured output — before you reach for fine-tuning.
Evaluation is the moat
If you cannot measure quality, you cannot improve it and you cannot defend it in front of legal. Invest in offline evals, online experiments and human review pipelines from day one.
Ship with Unisam
We build production AI systems as a senior custom software development company: retrieval, agents, evals, safety review and ongoing model maintenance.