Reliability in handling API failures, rate limits, and timeouts gracefully; cost control through caching, prompt optimization, and model selection to avoid runaway API costs; a way to evaluate whether outputs are actually good, not just "looks fine in testing"; guardrails through input validation and output filtering to prevent misuse; and monitoring through logging and alerting so you know when quality degrades in production.
Experience with the now-standard LLM application stack — vector databases, RAG architectures, prompt engineering frameworks, and evaluation tooling — means they're applying proven patterns to your specific data and use case rather than inventing the approach from scratch.
The risk isn't whether a contract engineer can build it — it's hiring someone who's only used AI tools to write code, versus someone who actually understands LLM application architecture, evaluation, and production hardening specifically.
Need vetted developers who already use AI tools well? Greatex Services places pre-vetted contract engineers across the US, UK, UAE, and ANZ — onboarded in days, not weeks.
Talk to Greatex Services