Advanced LLM Usage Patterns: Agentic Pipelines and Systematic Prompting

Expert LLM usage at the engineering level involves designing systems, not writing prompts. The key patterns are: orchestration with tool use (giving LLMs access to code execution, search, and external APIs via function calling, then building control flow around their tool selections), multi-agent architectures (specialized agents for planning, execution, verification, and critique running in coordinated pipelines), and structured output generation (using JSON mode or constrained decoding to produce machine-parseable outputs that feed downstream systems reliably). Memory management is a critical advanced skill: context windows are finite and expensive, so expert practitioners implement hierarchical memory (short-term context + long-term vector store retrieval) to give agents access to relevant history without bloating every prompt. Evaluation is the discipline that separates production-ready LLM systems from demos: building LLM-eval pipelines that measure task completion rate, factual accuracy, instruction following, and output format compliance — automatically, at scale — is what enables confident iteration. Key failure modes to engineer around include prompt injection (malicious instructions in retrieved content hijacking the agent), compounding errors in long chains (early mistakes propagate), and non-determinism (identical prompts produce different outputs across runs). Frameworks like LangGraph, CrewAI, and Anthropic's agent patterns provide scaffolding, but architectural judgment — knowing when to add an agent vs. simplify — remains the scarce expert skill.