A practical Spring Boot and MySQL design for retrying transactional Outbox events safely, handling at-least-once delivery, duplicate consumers, leases, backoff, and dead-letter recovery.
Use EXISTS and NOT EXISTS to filter orders, preserve accurate counts, and paginate before fetching details. Includes runnable SQL and Python checks.
Trace Spring AI tool calling from model request to Java execution. Test rejection, permission checks, repeated calls, and idempotency with a local example.
Trace Spring AI’s advisor chain with a focused request and response example, then verify where messages, tools, and memory enter the flow.
Debug Spring AI RAG when retrieved documents do not reach the model. Trace source labels, filter replacement, empty results and prompt context with Java 17.
Trace how Spring AI loads chat memory into model requests, and reproduce common issues with conversation IDs, message windows, and failed-call retries.
How developers can use AI coding tools while building durable skills in business understanding, system design, verification, and reliable delivery.
Trace service fulfillment back to sales, allocate stored-value payments, handle refunds, and calculate auditable partner commissions from confirmed facts.
Combine member profiles, rule-based tags, cohort behavior, and LLM explanations while keeping eligibility checks grounded in verified facts.