AI feature broken in production.Fixed this session.

PM assigned in 10 minutes. AI engineer starts diagnosing within the hour using your prompt logs, model response traces, and retrieval pipeline outputs. LLM hallucination in production is typically caused by: system prompt ambiguity that allows the model to fill gaps with invented facts, retrieval context that is too noisy or too short for the model to ground its answer correctly, missing guardrails that let the model answer outside its designated scope, or model temperature and sampling parameters set too high for a factual task. The engineer traces the exact failure mode, tightens the prompt or retrieval pipeline, adds factual grounding constraints, and tests against your regression set before the session closes.

RAG retrieval failures are resolved same session in the majority of cases. Root causes the engineer diagnoses: embedding model mismatch between indexing and query time (chunked with one model, queried with another), chunk size too large causing irrelevant context to dominate retrieved passages, missing metadata filters letting the pipeline pull documents from the wrong dataset partition, cosine similarity threshold set too low retrieving semantically distant chunks, and re-ranking not applied so the top-k set has low precision. The engineer audits your vector store configuration (Pinecone, Weaviate, Qdrant, pgvector), tests retrieval precision against your golden question set, and reindexes with corrected parameters. PM confirms expected precision targets before work starts.

Agent loop failures are diagnosed and fixed same session. The engineer inspects your agent trace logs to identify: a tool call returning an unexpected schema that the agent cannot parse, causing it to retry indefinitely; a missing stop condition in the agent loop allowing it to exceed max iterations without a valid termination state; a reasoning step producing a malformed action that the orchestrator rejects silently; or a context window overflow from accumulated conversation history causing the model to lose track of its original goal. Fixes applied depending on root cause: corrected tool output schemas, explicit loop termination guards, context pruning or summarization, and improved system prompt instruction for task completion criteria. PM scopes whether a single session resolves the issue or requires staged testing.

MCP (Model Context Protocol) development and custom AI connector work is in scope for a single session. The engineer builds: MCP server implementations exposing your internal APIs as tool-callable surfaces for Claude and other MCP-compatible models, custom connector adapters translating between your data schemas and LLM-expected formats, authentication middleware for secure tool calls (API key, OAuth2, JWT), and streaming response handlers for long-running tool executions. PM scopes the connector surface area in the first 15 minutes number of tools, authentication method, error handling requirements and gives you a timeline before work starts. Most single-connector MCP integrations are delivered in one 4-hour session.

Every QuickHire AI engineer passes a live debugging exercise on a broken LLM application real RAG pipeline, real vector store, time-limited. They must identify whether the failure is in retrieval, context assembly, prompt construction, or model parameters, and implement a fix without hints. The written assessment covers: prompt engineering patterns (chain-of-thought, few-shot, system prompt design, output formatting constraints), vector database operations (embedding model selection, chunking strategy, similarity search tuning), AI agent architecture (tool calling, memory management, multi-agent coordination), LLM API usage (token budgeting, streaming, error handling, rate limit management), and AI security (prompt injection defense, output validation, data leakage prevention). Only the top 3% of applicants pass all stages. PM matches specifically to your model provider, framework (LangChain, LlamaIndex, DSPy, raw API), and stack.