Shipping Real‑World IA: How I Build Maintainable LLM Systems

When teams talk about “adding AI,” they often imagine magic. In practice, shipping reliable IA means disciplined engineering: clear objectives, good data, fast feedback, and ruthless cost control. Here’s my field guide from projects like EHVA.ai and Prepsponsor.

1) Start with a measurable outcome

Pick a north‑star KPI (first‑call resolution, lead conversion, reply rate). Everything—from prompts to infra—serves that KPI.

2) Design the data flywheel

Grounding: RAG over curated knowledge (policies, FAQs, product docs).

Storage: pgvector or Pinecone, with typed metadata to control recall.

Quality: ingestion pipelines that de‑duplicate, chunk well, and label edge cases.

3) Make serving boring (on purpose)

APIs: Laravel for clean contracts and auth; background jobs for retries.

Orchestration: LangChain/LangGraph for tools, guards, and routing.

Frontends: Vue/React dashboards for ops, evals, and red‑team review.

4) Close the loop with evals

Offline: regression suites for prompts and tools.

Online: human‑in‑the‑loop, thumbs‑up/down, and targeted re‑prompts.

Metrics: latency, cost per task, accuracy by intent, deflection rate.

5) Control cost without harming quality

• Cache embeddings and responses.
• Compress context windows; prefer retrieval over long prompts.
• Use model routing (fast/cheap vs. slow/accurate) with fallbacks.

A practical checklist

✓ Objective: one sentence, one metric
✓ Data: version‑controlled, tagged, deduplicated
✓ Infra: API → orchestration → vector store → model
✓ Eval: offline + online, with real user feedback
✓ Cost: cached, compressed, routed

If you want help turning an AI idea into a dependable product, I can audit your current stack and ship a pilot in weeks—not months. Let’s talk: dleolopez.dev