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R-23
Intelligence (AI)
Production-Grade LLM Applications: Prompt Engineering Best Practices
Prompt templates, chain-of-thought prompting, hallucination prevention, and RAG patterns.
PAR2 Labs
August 12, 2024
10 min
Artificial intelligence is the most over-promised and under-engineered layer in most modern products. The gap between a striking demo and a system people will actually rely on is enormous — and it is exactly where the real work lives.
01
Latency, cost, and the bill nobody mentions
Cost and latency are features, not afterthoughts. Caching, smaller models for the easy cases, and escalation only for the hard ones keep the experience fast and the monthly bill sane.
We instrument token spend per request from day one. Knowing your unit economics early is the difference between an AI feature that scales and one that quietly bankrupts its own business case.
02
Keeping reasoning legible
People trust what they can inspect. Citations, intermediate steps, and the ability to ask “why did you say that?” turn a black box into a colleague worth keeping.
Legibility keeps your own team honest too. A system you can explain is a system you can debug, audit, and improve with intent rather than guesswork.
03
Human in the loop, by design
Automation earns trust when it knows its limits. The strongest deployments route confident cases through automatically and reserve human attention for the genuinely ambiguous ones.
That balance is a product decision, not a technical default. Get it right and your team scales; get it wrong and you either drown in review queues or ship confident mistakes.
Capability is cheap. Trust is the moat — and trust is engineered, not prompted.
04
Data is the real moat
Models are increasingly commoditised; the proprietary data and feedback loops around them are not. The systems that compound are the ones that get smarter every time they are used.
We design capture from the start — every correction, every override, every thumbs-down becomes training signal for the next iteration instead of being thrown away.
05
Shipping, then operating
AI features are never “done” — they are operated. Instrument everything, watch real usage, and feed what you learn straight back into your evaluation set.
The teams that win treat launch as the start of the work, not the finish line. Monitoring, drift detection, and retraining cadence matter more than the cleverness of the first release.
06
The bottom line
The most advanced model in the world is worthless if no one is willing to act on its output. Reliability, transparency, and graceful failure aren't constraints on capability — they're what let capability ship.
At PAR2 LABS we build AI for the moment it matters, not the moment it demos.
Key Takeaways
01
Treat latency and cost as first-class features, not afterthoughts.
02
Capture every correction as training signal — the loop is the moat.
03
Launch is the start of the work; instrument, monitor, and retrain.
04
Map failure modes before features — behaviour at the edges defines the product.
PAR2 Labs · Intelligence (AI)
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