AI feature broken in production.Fixed this session.

PM assigned in 10 minutes. AI specialist starts diagnosis within the hour. LLM hallucinations in production are almost always one of three causes: a system prompt that is too permissive allowing the model to answer outside its knowledge domain, a RAG retrieval pipeline returning irrelevant chunks so the model tries to answer from incorrect context, or a context window that is too long or poorly structured causing the model to ignore critical instructions. The engineer runs your failing queries through each pipeline layer separately to isolate the failure, then delivers a fix with RAGAS evaluation scores showing before-and-after accuracy improvement on your real production queries.

Yes. Rate limit errors are a standard Starter session fix resolved in under 4 hours. The engineer implements: exponential backoff with jitter (the industry-standard pattern for rate limit handling), request queuing to smooth burst traffic rather than hitting the API in parallel, per-user or per-endpoint rate limit budgets to prevent one heavy user from exhausting the shared pool, and optionally a caching layer for identical or near-identical queries that do not need a fresh LLM call every time. For OpenAI rate limits, the engineer also evaluates whether upgrading your tier or switching from gpt-4 to a faster tier-2 model for non-critical queries resolves the problem more efficiently than pure retry logic.

Yes. PM scopes the full cycle into a Full Day session: diagnosis of the accuracy failure (15 minutes), root cause identification (30 minutes), prompt engineering or retrieval improvement (2-3 hours), evaluation against a test set of your real queries using RAGAS or a custom eval suite, then production deployment with monitoring. The session ends when before-and-after accuracy scores prove the improvement and the fix is live in production. For RAG accuracy fixes, the engineer also implements a confidence threshold if retrieval scores fall below a certain level, the model responds with a graceful fallback rather than hallucinating an answer from weak context.

Yes two Full Day sessions back to back is a realistic timeline for a working AI feature demo. In 48 hours the engineer can build: a production-ready LLM chatbot with your knowledge base (RAG pipeline, vector store, streaming responses), a document summarization feature with structured output, an AI-powered semantic search replacing keyword search, or a content generation feature with output validation and brand voice controls. PM scopes precisely what is achievable in session 1 no surprises on day 2. The deliverable is a demo-safe feature deployed to your staging environment, not a local prototype that only runs on the engineer's machine.

Every QuickHire AI engineer passes a vetting process specifically designed for production AI systems: a written assessment covering LLM API integration, RAG architecture (vector database selection, chunking strategy, embedding models, re-ranking), agent design patterns (LangChain, LangGraph, CrewAI, AutoGen), evaluation methodology (RAGAS, LLM-as-judge, human evaluation), and MLOps for model deployment. Plus a live debugging exercise on a broken RAG pipeline in a real environment the candidate must diagnose retrieval failures under time pressure. Only the top 3% pass. PM matches to your specific LLM provider (OpenAI, Anthropic, Google, Mistral, or open-source self-hosted) and framework before the session starts.