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Structural choices inside an LLM application: whether a failure calls for a better prompt, retrieval or fine-tuning, and when splitting work across several agents is worth the coordination cost. Each guide starts from a measured baseline rather than a preferred technique.
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Use several AI agents when a task has well-defined subtasks and a measured single-agent baseline falls short. Coordination costs tokens, time and engineering, so budget every run and let evidence decide.
Choose between prompt engineering, RAG and fine-tuning by diagnosing why answers fail: unclear instructions, missing knowledge or a consistent behavior problem. Then apply the smallest change that fixes the measured failure.
