Skill Guide

Prompt engineering and LLM orchestration (chain-of-thought, few-shot, system prompts)

The systematic practice of designing, testing, and optimizing inputs (prompts) and workflows (orchestration) to extract reliable, high-quality, and contextually accurate outputs from large language models (LLMs).

It directly translates to cost efficiency by reducing API calls and compute waste, and drives product quality by ensuring consistent, predictable model behavior in customer-facing applications and internal tools.

1 Careers

1 Categories

8.7 Avg Demand

20% Avg AI Risk

How to Learn Prompt engineering and LLM orchestration (chain-of-thought, few-shot, system prompts)

1. Master zero-shot and basic few-shot prompting structures. 2. Understand and implement system prompt roles for persona and constraint setting. 3. Learn basic Chain-of-Thought (CoT) to force logical reasoning steps.

1. Move to complex orchestration: building sequential, conditional, and branching prompt chains. 2. Implement prompt versioning and A/B testing frameworks. 3. Learn to parse and validate structured output (JSON, XML) from LLMs reliably. Common mistake: over-engineering prompts before validating the core task.

1. Architect multi-agent systems with specialized prompts and managed state/memory. 2. Design evaluation pipelines (LLM-as-a-judge) and human-in-the-loop feedback systems. 3. Align prompt strategy with business KPIs (latency, cost, accuracy) and mentor teams on prompt hygiene and governance.

Practice Projects

Beginner

Project

Build a Constrained Q&A Bot

Scenario

Create a bot that only answers questions about Python programming from a specific list of documentation (e.g., the official Python tutorial), refusing off-topic queries.

How to Execute

1. Draft a system prompt defining the bot's persona, scope, and refusal behavior. 2. Write 3-5 few-shot examples showing correct answers and polite refusals. 3. Implement basic error handling for ambiguous queries. 4. Test with edge-case questions outside the domain.

Intermediate

Project

Orchestrate a Research Summarizer with Verification

Scenario

Build a pipeline that takes a research paper PDF, extracts key sections, generates a summary in plain language, and then uses a second prompt to fact-check the summary against the original text.

How to Execute

1. Use a text extraction tool to get the raw content. 2. Create Prompt 1: A few-shot prompt to generate a structured summary (key findings, methodology, limitations). 3. Create Prompt 2: A Chain-of-Thought prompt that takes the summary and original text, requiring the model to verify each claim line-by-line. 4. Build a simple Python script to chain these steps and log outputs for each stage.

Advanced

Project

Design a Multi-Agent Customer Support Triage System

Scenario

Architect a system where a primary 'Router' agent classifies customer emails into categories (Billing, Technical, Shipping) and dispatches them to specialized 'Solver' agents, each with its own knowledge base and persona, requiring consensus on ambiguous cases.

How to Execute

1. Design the system prompt for the Router agent to output structured classification with a confidence score. 2. Develop specialized Solver agent prompts with few-shot examples and retrieval-augmented generation (RAG) context. 3. Implement a state management logic to handle handoffs and a 'Moderator' agent to resolve low-confidence classifications. 4. Build an evaluation framework measuring accuracy, latency, and cost per resolution.

Tools & Frameworks

Prompt Development & Management

LangChain PromptTemplate / LCELPromptLayerOpenAI Playground / Anthropic WorkbenchWeights & Biases Prompts

For templating, versioning, logging, and systematically testing prompt variations across models. Use PromptLayer or W&B for tracking prompt performance metrics over time.

Orchestration Frameworks

LangChain / LangGraphLlamaIndex (for RAG)AutoGen / CrewAISemantic Kernel

For building complex, multi-step chains, managing conversation state, and orchestrating multiple agents. LangGraph is particularly useful for stateful, cyclic workflows.

Evaluation & Monitoring

RagasDeepEvalPhoenix (by Arize AI)Custom LLM-as-a-Judge pipelines

For quantitatively evaluating prompt/chain output quality (factuality, relevance, coherence) in production. Ragas is specialized for RAG system evaluation.

Interview Questions

Answer Strategy

Use a structured debugging framework. Sample answer: 'I would first check for data drift by analyzing production query distributions versus the test set. Next, I'd inspect the input cleaning pipeline for subtle format changes. Then, I would audit the context window management-is relevant context being truncated or replaced? Finally, I'd implement a logging and sampling system to categorize failure modes, likely revealing an edge-case trigger that the prompt's instructions or few-shot examples don't cover.'

Answer Strategy

Tests understanding of when to apply advanced techniques. Sample answer: 'For a medical triage bot analyzing symptoms, I used CoT to force the model to reason through differential diagnoses explicitly before suggesting an urgency level. The trade-off was increased latency and token cost, but it was necessary for safety and auditability. We mitigated cost by only using CoT for complex cases, first routing simple queries through a faster classifier.'