Generative AI in Enterprise:
Beyond the Hype.

The enterprise AI landscape has fundamentally shifted with transformer-based architectures. We're moving beyond simple chatbots to sophisticated document processing pipelines, RAG systems, and domain-adapted LLMs.

The RAG Revolution

Retrieval-Augmented Generation (RAG) combines generative AI with your proprietary data. Instead of relying solely on pre-trained knowledge, RAG systems fetch relevant context from vector databases before generating responses.

This architecture enables LLMs to provide accurate, up-to-date answers grounded in your organization's specific knowledge base—without expensive fine-tuning.

Production Deployment Strategies

Moving from prototype to production requires careful attention to latency, cost, and reliability. A typical RAG query involves:

• Embedding generation: ~50ms
• Vector search: ~20ms
• LLM inference: ~500-2000ms

The Modern AI Tech Stack

Building production-grade AI systems requires the right combination of tools:

Enterprise AI Governance

Compliance frameworks like GDPR and SOC 2 require robust AI governance. Key considerations include:

Guardrails: Constitutional AI approaches and output filtering ensure responses stay within bounds. Implement classifier models that screen outputs for sensitive information, brand alignment, and factual accuracy before delivery to end users.

Audit logging: Comprehensive logging of all LLM interactions for compliance and debugging. Every prompt, response, retrieved context, and model version should be captured with timestamps and user attribution.

LLMOps: Treat model deployments with the same rigor as traditional MLOps—A/B testing, canary rollouts, and automated evaluation pipelines using frameworks like RAGAS or DeepEval.

Fine-Tuning for Domain Adaptation

While prompt engineering suffices for general tasks, specialized domains like legal, medical, or financial services benefit from targeted fine-tuning approaches.

LoRA (Low-Rank Adaptation) and QLoRA represent parameter-efficient methods that require only 0.1-1% of parameters to be trainable. This reduces GPU memory requirements from 80GB+ to under 24GB while achieving near full fine-tuning performance.

The fine-tuning workflow typically involves: (1) curating domain-specific training data, (2) formatting conversations in the model's expected structure, (3) running training with appropriate hyperparameters, and (4) evaluating against held-out test sets using domain-relevant metrics.

Cost Optimization Strategies

Enterprise AI deployments can quickly become expensive without proper optimization. Key strategies include:

• Model routing — Use smaller models for simple queries, reserving expensive models for complex reasoning
• Prompt compression — Reduce token counts through context summarization and key information extraction
• Semantic caching — Cache responses for semantically similar queries to reduce API calls by 40-60%
• Batch processing — Group non-urgent requests to leverage batch API pricing (often 50% cheaper)

Measuring Success: Key Metrics

Enterprise AI systems require comprehensive monitoring across multiple dimensions: