Skill Guide

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

The systematic design, testing, and orchestration of natural language inputs (prompts) to reliably guide large language models (LLMs) toward desired outputs, using structured techniques like system prompts, few-shot examples, chain-of-thought reasoning, and ReAct action loops.

It directly translates to the quality, reliability, and cost-efficiency of LLM-powered products and workflows, making it a core competency for building valuable AI features. Mastery reduces iteration time, minimizes hallucinations, and enables the creation of complex, multi-step AI agents.

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

1. Master core terminology: understand system prompts, user prompts, temperature, top-p, and token limits. 2. Learn the anatomy of a basic instruction prompt (Role, Task, Context, Format). 3. Practice decomposing vague user requests into clear, structured LLM instructions.

1. Implement few-shot prompting with curated examples, focusing on edge cases. 2. Apply chain-of-thought (CoT) to solve multi-step reasoning tasks like math word problems or code debugging. 3. Build a simple ReAct agent that uses tools (e.g., calculator, web search) by defining explicit Thought/Action/Observation loops. Avoid the mistake of over-engineering prompts before validating the task's feasibility.

1. Design and maintain prompt libraries and orchestration pipelines for production systems, ensuring version control and A/B testing. 2. Develop evaluation frameworks (using metrics like faithfulness, answer relevance) to systematically benchmark prompt variants. 3. Architect complex agentic systems (e.g., AutoGPT-style) that combine multiple prompt patterns, memory modules, and tool use, while managing cost and latency.

Practice Projects

Beginner

Project

Craft a Multi-Role System Prompt for a Customer Service Bot

Scenario

Create a system prompt that instructs an LLM to act as a helpful, empathetic customer service agent for an e-commerce company. The bot must handle order status inquiries, product questions, and returns, while escalating sensitive issues to a human.

How to Execute

1. Define the bot's persona, tone, and strict boundaries (e.g., 'never make up information'). 2. Write a few-shot example demonstrating how to handle a standard order inquiry and an escalation request. 3. Integrate the system prompt with a simple API call using a tool like the OpenAI Playground. 4. Test with 5 different user inputs, including ambiguous ones, and refine the prompt based on outputs.

Intermediate

Project

Build a ReAct-Style Research Assistant Agent

Scenario

Develop an agent that can take a complex research question (e.g., 'Compare the market share of the top 3 cloud providers in 2023'), use a web search API to find information, and synthesize a cited answer.

How to Execute

1. Define the ReAct prompt template with explicit placeholders for Thought, Action, and Observation. 2. Integrate with a real search API (e.g., SerpAPI). 3. Implement a Python script that runs the prompt in a loop, parses the LLM's 'Action', executes the corresponding tool, and feeds the 'Observation' back into the next prompt. 4. Test the agent with 3 different research questions and debug any looping or parsing errors.

Advanced

Project

Orchestrate a Prompt Pipeline for Financial Document Analysis

Scenario

Design a system to automatically extract key metrics (revenue, net income), assess sentiment from management discussion sections, and generate a summary report from a 10-K SEC filing PDF.

How to Execute

1. Architect a multi-step pipeline: a) Document parsing and chunking prompt, b) Extraction prompt with structured JSON output, c) Sentiment analysis prompt using CoT, d) Summarization prompt. 2. Use a framework like LangChain or LlamaIndex to orchestrate the prompts and manage document embeddings for retrieval. 3. Implement rigorous evaluation by comparing extracted data against a manually-annotated golden dataset. 4. Containerize the pipeline and build a simple UI for batch processing.

Tools & Frameworks

LLM APIs & Platforms

OpenAI API (GPT-4, function calling)Anthropic Claude API (large context, XML tags)Google Vertex AI Gemini APIHugging Face Inference Endpoints

Primary interfaces for testing and deploying prompts. Use function calling (OpenAI) or tool use (Anthropic) for structured ReAct patterns. Choose based on context window size, cost, and speed.

Orchestration Frameworks

LangChainLlamaIndexHaystackMicrosoft Semantic Kernel

Libraries for building complex chains of prompts, integrating with external tools/data sources, and managing memory. Essential for moving from single-prompt experiments to production agentic systems.

Evaluation & Testing

PromptfooLangSmithRagasDeepEval

Tools for systematic prompt evaluation. Promptfoo and LangSmith offer tracing and benchmarking. Ragas/DeepEval specialize in evaluating RAG pipeline faithfulness and relevance.

Mental Models & Methodologies

CRISPE Framework (Capacity, Role, Insight, Statement, Personality, Experiment)COSTAR Framework (Context, Objective, Style, Tone, Audience, Response)Prompt ChainingFew-Shot Selection via Embedding Similarity

Structured approaches for designing prompts. CRISPE/COSTAR ensure all critical components are considered. Chaining and embedding-based example selection are key techniques for intermediate/advanced orchestration.

Interview Questions

Answer Strategy

Focus on the systematic approach: defining output schema (JSON), using few-shot examples to teach format, implementing chain-of-thought for disambiguation, and post-processing validation. Sample Answer: 'I'd start by defining a strict JSON schema for the output. I'd then craft 3-4 few-shot examples covering common formats and edge cases like missing fields or ambiguous entries. The prompt would include a CoT instruction like: "First, identify all potential personal data points. Then, match them to the schema fields, noting any uncertainty. Finally, output the JSON." For production, I'd add a validation layer to check JSON correctness and flag low-confidence extractions for human review.'

Answer Strategy

Tests debugging skills for agentic systems. The candidate should discuss prompt analysis, loop detection, and reflection mechanisms. Sample Answer: 'I'd first analyze the prompt logs to see if the Thought or Observation sections are providing clear feedback. The fix likely involves one of three things: 1) Improving the prompt's instructions to include a "reflection" step where the agent evaluates if an action was productive, 2) Adding explicit stop conditions or a maximum loop counter, or 3) Providing better few-shot examples that demonstrate how to recover from ineffective actions.'