90% of AI Engineering in 2026 is mastering these 10 concepts (the rest is bikeshedding model choice): 1) Contracts, not prompts Treat the LLM call like an API: schema in/out, enums, max lengths, and unit tests for edge cases. 2) Latency and cost budgets p95 tokens drive UX and bill. Cache aggressively, stream responses, and set hard cutoffs per route. 3) Data flywheel and provenance Know what went into the response: docs version, embedding model, filters, and why a chunk was retrieved. 4) Retrieval that survives production Chunking, overlap, metadata, and hybrid search beat clever prompts; reindexing needs to be safe and incremental. 5) Evaluation you can run in CI Golden sets + task metrics (exact match, citation precision, jailbreak rate). Humans for the last mile, not the first. 6) Debugging nondeterminism Capture full traces: prompt, tools called, retrieved context, temperature, and random seeds when supported. 7) Tool use and state management Tools fail. Make every tool call idempotent, add timeouts, and persist state so retries don’t double-charge or double-write. 8) Guardrails and security boundaries Treat model output as untrusted input: sandbox code exec, restrict egress, redact secrets, and log prompt injection attempts. 9) Observability that answers why, not just what Per-request spans across retriever, reranker, model, and tools; track token counts, cache hit rate, and refusal reasons. 10) Common real-world failure modes Context stuffing that hides the answer, retrieval drift after doc updates, and silent prompt changes that tank evals overnight