Skill Guide

Prompt engineering and system prompt design for multi-step agents

The discipline of architecting layered instruction sets and state management logic to direct large language models through complex, multi-stage reasoning and action loops with controlled output.

This skill directly translates into operational efficiency by enabling the automation of complex knowledge work, reducing error rates in AI-driven processes, and scaling expert-level decision-making across an organization.

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9.1 Avg Demand

25% Avg AI Risk

How to Learn Prompt engineering and system prompt design for multi-step agents

Master single-turn prompt patterns (e.g., chain-of-thought, few-shot examples). Understand core concepts: temperature, top-p, token limits, and the role of a system prompt. Build a habit of iterative testing and version control for prompts.

Design prompts that handle state and context management across multiple API calls. Implement common agent architectures like ReAct (Reason + Act) or Plan-and-Solve. Focus on error handling, fallback strategies, and parsing structured outputs (JSON, XML).

Architect self-correcting and self-evaluating agent loops. Integrate tool use (APIs, code execution) and design prompts for meta-cognitive tasks like prompt rewriting or strategy selection. Align agent design with business process re-engineering and risk governance frameworks.

Practice Projects

Beginner

Project

Build a Multi-Step Research Assistant

Scenario

Create an agent that, given a research topic, will generate a search query, synthesize the top 3 results, and produce a structured summary with citations.

How to Execute

1. Define the agent's goal and required output format. 2. Write a system prompt that sets the persona and outlines the step-by-step process (query generation -> summary -> citation). 3. Implement the workflow using a framework like LangChain's LLMChain or a simple Python script with sequential API calls. 4. Test with diverse topics and refine prompts for consistency and accuracy.

Intermediate

Project

Implement a ReAct-Style Customer Support Agent

Scenario

Design an agent that can reason about a customer's issue (e.g., 'order not delivered'), decide on an action (e.g., 'check_order_status'), execute it via a simulated API, and iterate until resolution.

How to Execute

1. Define the Thought, Action, Observation format in the system prompt. 2. Create mock tool functions (e.g., check_order_status(order_id)). 3. Implement a loop that feeds the agent's output back as context until a final answer is determined. 4. Focus on prompt engineering to guide the agent's reasoning and handle edge cases like invalid tool calls or ambiguous requests.

Advanced

Project

Design a Self-Refining Content Generation Pipeline

Scenario

Build a system where one agent drafts marketing copy, a second agent critiques it against brand guidelines, and a third agent synthesizes the feedback to revise the draft, operating in a loop until quality thresholds are met.

How to Execute

1. Architect the multi-agent system with distinct roles (Writer, Critic, Editor). 2. Engineer system prompts that enforce role-specific behaviors and quality criteria (e.g., Critic must provide structured feedback with severity ratings). 3. Implement a control loop with convergence logic (e.g., stop after 3 iterations or when Critic's approval score > 8/10). 4. Integrate automated evaluation metrics (readability scores, keyword inclusion) alongside LLM-as-a-judge for robust assessment.

Tools & Frameworks

Software & Platforms

LangChain / LangGraphOpenAI Assistants APIAutoGen / CrewAI

Use LangChain for composable prompt chains and LangGraph for complex stateful agent workflows. The Assistants API provides built-in tool use and threads. AutoGen and CrewAI facilitate multi-agent conversation and collaboration patterns.

Mental Models & Methodologies

ReAct FrameworkPlan-and-Solve PromptingReflexion / Self-Refine

Apply ReAct to ground reasoning in observable actions. Use Plan-and-Solve for decomposing complex tasks upfront. Implement Reflexion to enable agents to reflect on and learn from past failures within a session.

Testing & Evaluation

PromptLayer / HeliconeRagas (for RAG agents)Human-in-the-Loop (HITL) checkpoints

Use observability platforms to track prompt performance and costs. Ragas provides metrics for retrieval-augmented generation quality. Design critical decision points where human oversight is required before agent execution.

Interview Questions

Answer Strategy

Structure the answer around the agent's core loop (Reason -> Act -> Observe). The sample answer should explicitly define the system prompt's role, the thought process for tool selection, and a fallback strategy (e.g., retry with reformulated query, escalate to human).

Answer Strategy

Tests debugging methodology and iterative improvement. The candidate must demonstrate a systematic approach: tracing the failure to a specific step (reasoning, tool use, synthesis) and referencing prompt engineering techniques (e.g., adding step-back prompting, clarifying instructions, adding constraints).