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Engineering8 min read1touch.ai Research

Deploying LLMs in Production: What Enterprises Get Wrong

Most enterprise LLM deployments fail not because of model quality, but because of inadequate infrastructure, evaluation frameworks, and change management.

Most enterprise LLM deployments fail not because of model quality, but because of inadequate infrastructure, evaluation frameworks, and change management.

The Evaluation Gap

Teams that perform well in demos frequently struggle in production because they evaluated their LLM on cherry-picked examples rather than representative real-world inputs. Production evaluation needs a diverse test set, automated metrics that correlate with business outcomes, and human review protocols.

Infrastructure Underinvestment

LLMs have different infrastructure requirements than traditional software. Build LLM infrastructure as a platform capability — with prompt management, caching, fallback routing, and cost monitoring.

The Context Problem

Organizations achieving the best results invest in RAG architectures connected to internal knowledge bases, combined with domain-specific fine-tuning.

Change Management

Organizations that invest in training, escalation protocols, and feedback mechanisms achieve far higher adoption than those that simply release tools.

LLMProduction AIEnterpriseMLOps

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