Building Production AI: A Practical Guide

AI is now becoming part of real products. The strategic choices you make up front determine whether AI drives growth or becomes a compliance and cost burden. Let’s have a look at the most important considerations before you start building.

1. Factual correctness.
   Wrong answers break trust. Use Retrieval-Augmented Generation (RAG) to retrieve your documents, feed exact passages to the model, and log the sources and provenance. That’s the fastest, most reliable way to reduce hallucinations.
2. Speed.
   Users expect near-instant responses. Small Language Models (SLMs) generally provide lower latency and more predictable costs, so start there and scale only when business metrics demand it (Examples to evaluate: Microsoft’s Phi family, Google’s Gemma family, …)
3. Consistent response style.
   Tone, format and policy adherence matter. Use prompt engineering for fast control, LoRA/adapters for light per-customer behavior, and fine-tuning or RLHF only when you need deep, repeatable alignment.

Getting AI to match your organization's voice and knowledge requires a systematic approach to context and customization.

• Build a vector index of your content. 
• Use RAG to feed vetted context into each prompt and always return source IDs.
• Apply prompt templates and server-side JSON/schema validation to enforce output shape.
• Use LoRA/adapters for quick, low-cost per-customer customization. Consider fine-tuning or RLHF for large-scale alignment needs.

Your hosting strategy impacts everything from compliance to costs. You can call hosted APIs (OpenAI, Anthropic, vendor-hosted APIs) or run models yourself. Managed platforms like Azure AI Foundry and Google Vertex simplify enterprise controls and offer more features, but may increase the risk of vendor lock-in. Model hubs and providers (Hugging Face, Mistral, Aleph Alpha) let you self-host for full data residency and pricing predictability. Choose the path that matches your compliance and procurement constraints, and keep migration in mind (an adapter layer and exportable artifacts make it easier). Procurement quick check: before you sign, confirm DPA & subprocessors, whether vendor inputs are used for model training, region and data-residency guarantees, log/embedding exportability, and retention policies.

Similar to any production software, AI can become a burden without proper operations. Make sure you have good visibility and are ready to scale.

• Version model, adapter and encoder. Run regression and red-team checks.
• Canary rollouts and fast rollback paths.
• Per-request logs: input, prompt, retrieved source ids, model & adapter ids, moderation flags.
• An exportable audit pack for legal and procurement.

Implementing AI successfully in production requires building a system tailored to your specific needs, rather than simply adopting the latest model. Start with SLM and retrieval. Add adapters where you need specialization, and plan for agentic workflows as the next stage. Wrap everything in MLOps and auditability so AI actually scales with your business.