Building Production-Ready LLM Applications: Lessons Learned

The LLM Production Gap

After deploying 10+ LLM-powered features to production over the past year, we've learned that the gap between a working prototype and a production system is enormous. The demo that wowed stakeholders in a meeting often fails spectacularly when real users get their hands on it.

Here's what we've learned about building LLM applications that actually work at scale.

1. RAG is Table Stakes, Not a Silver Bullet

Retrieval Augmented Generation (RAG) has become the default pattern for giving LLMs access to custom data. But vanilla RAG implementations often disappoint. Here's what actually works:

Chunk Strategically

Don't just split documents every 512 tokens. Consider semantic chunking, overlapping chunks (10-20% overlap), and metadata preservation.

Hybrid Search Wins

Pure vector similarity search misses obvious matches. We use BM25 for keyword matching, vector search for semantic similarity, and re-ranking with a cross-encoder.

2. Prompt Engineering is Engineering

Treat prompts like code. Version them. Test them. Review them. We built an internal framework that runs every prompt change against 100+ golden test cases.

3. Hallucination Mitigation

LLMs will hallucinate. Accept it. Build around it with citation requirements, confidence scoring, and human-in-the-loop for high-stakes decisions.

4. Latency Matters More Than You Think

Users abandon chatbots after 3 seconds. Stream responses, cache embeddings, and consider smaller models for simpler use cases.

5. Evaluation is Hard but Essential

We use human evaluation, user signals, and A/B testing. The teams that win aren't the ones with the cleverest prompts—they're the ones with the best engineering practices.