Spring AI Tool Calling: From Model Request to Java Method

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 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.

Combine member profiles, rule-based tags, cohort behavior, and LLM explanations while keeping eligibility checks grounded in verified facts.

Design a reviewable training-plan workflow from longitudinal records, fitness tests, historical plans, and an exercise library.

A practical online classroom architecture for media failover and courseware fallback, with clear recovery boundaries for Agora, ZEGO, classroom state, and post-lesson replay.

A small QLoRA experiment for support-ticket classification, covering configuration, observed results, and the limits of loss-based evaluation.

Explain self-attention, decoder-only transformer blocks, and the checks needed to connect model architecture with real inference behavior.