Getting generative AI from a demo to a production system follows the same build-deliver-operate-run cycle we know from DevOps, but each phase has new wrinkles. The prompt is never just one call – it is a chain of system prompts, few-shot examples, chain-of-thought reasoning, structured output formatting, schema validation, and retries. That single user question might translate into a dozen LLM calls under the hood.
Model selection is a multi-dimensional problem. Context window size, time-to-first-token latency, parameter count, pricing per token, and whether it will keep your data private all factor in. We started categorizing use cases like bronze-silver-gold: some tasks need the best model, others work fine with a cheaper one. Open source models are viable now, and platforms like Hugging Face serve as the GitHub of models. The key insight: most teams should use off-the-shelf models rather than training their own.
RAG solves the privacy problem while keeping data current. Instead of fine-tuning the model with your proprietary information, you inject relevant document chunks into the prompt at runtime. This means access control, real-time updates, and no data leakage into model weights. But getting it right requires intelligent chunking – split by document structure, not arbitrary byte counts – and good embedding models to find semantically relevant pieces. We found hybrid search (combining keyword and semantic results) worked better than forcing users to switch from keywords to questions.
The observability story goes beyond traditional metrics. Yes, track latency, cost, and throughput. But the metrics that matter are data quality metrics: is the answer relevant, is it toxic, does it contain PII, what is the sentiment? You need LLMs evaluating LLMs, specialized models for security checks, and continuous production testing with curated test sets. Version your prompts. A/B test them. Capture user feedback through UI signals like copy buttons and regenerate actions.
Earning customer trust requires AI company principles published transparently, clear opt-in communication, and UI cues that signal AI-generated content – different colors, sparkle icons, even deliberately slowed text rendering. Treat your data as a dependency with proper registry and tracking. Adapt your pricing to meter AI usage. And recognize that whatever you build yourself today will likely become a vendor commodity within months. Start the journey anyway – the learning compounds.
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