Skill Guide

Prompt pattern design - zero-shot, few-shot, chain-of-thought, ReAct, tree-of-thought, and self-consistency techniques

Prompt pattern design is the systematic engineering of instructions for large language models (LLMs) to guide their reasoning and output generation using specific paradigms like zero-shot (no examples), few-shot (with examples), chain-of-thought (step-by-step reasoning), ReAct (reasoning + acting), tree-of-thought (parallel reasoning paths), and self-consistency (majority voting).

This skill is critical for maximizing the performance, reliability, and cost-efficiency of LLM applications. It directly impacts business outcomes by transforming a general-purpose AI into a precise, predictable, and high-quality task executor, reducing hallucinations and operational costs.

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How to Learn Prompt pattern design - zero-shot, few-shot, chain-of-thought, ReAct, tree-of-thought, and self-consistency techniques

1. Master the core definitions and mechanics of each technique (zero-shot vs. few-shot, CoT vs. ToT). 2. Practice decomposing complex tasks into sequential reasoning steps for basic CoT. 3. Understand when each pattern is most effective based on task type (classification vs. creative writing vs. complex problem-solving).

1. Move to hybrid approaches: Combine few-shot with CoT for complex tasks. 2. Implement ReAct patterns for tasks requiring external tool interaction (e.g., API calls, search). 3. Focus on iterative evaluation: Use metrics (accuracy, coherence) to A/B test prompt variations and avoid common pitfalls like prompt injection or overly rigid instructions.

1. Design orchestration systems that dynamically select the optimal prompt pattern based on the input query's complexity. 2. Architect self-consistency pipelines for mission-critical outputs (e.g., medical diagnosis support, financial analysis). 3. Mentor teams on prompt governance and develop internal libraries of reusable, validated prompt templates.

Practice Projects

Beginner

Project

Building a Zero-Shot & Few-Shot Classifier

Scenario

You need to classify customer support emails into categories: 'Billing Issue', 'Technical Bug', or 'Feature Request' without fine-tuning a model.

How to Execute

1. Write a zero-shot prompt that provides the label definitions and asks for classification. 2. Curate 2-3 labeled examples for each category. 3. Construct a few-shot prompt incorporating these examples. 4. Test both prompts on a held-out email set and compare accuracy and consistency.

Intermediate

Project

Implementing a ReAct Agent for Data Retrieval

Scenario

Build an agent that can answer factual questions like 'What was the revenue growth of Company X in Q3 2023?' by reasoning and searching a local knowledge base.

How to Execute

1. Design the ReAct prompt template: 'Thought: [reasoning], Action: [search_query], Observation: [result]'. 2. Define the action space (e.g., `search_kb(query)`). 3. Implement a simple Python loop to feed the model's output back into the prompt until it returns a final 'Answer'. 4. Test on questions requiring multi-step retrieval.

Advanced

Project

Designing a Self-Consistency System for Critical Analysis

Scenario

You are building a system to generate investment memo summaries where consistency and reduced bias are paramount.

How to Execute

1. Craft a primary Chain-of-Thought prompt for detailed analysis. 2. Create 3-5 variations of this prompt (different phrasings, emphasis). 3. Write a script to run each prompt variation on the same input, collecting all outputs. 4. Implement a voting or similarity-checking function to select the most consistent summary, or synthesize a final version.

Tools & Frameworks

Software & Platforms

OpenAI API (or any major LLM API)LangChain (for ReAct, ToT orchestration)Python (for scripting evaluations and pipelines)

Use these to programmatically test, deploy, and scale prompt patterns. LangChain is particularly valuable for implementing complex agentic patterns like ReAct and self-consistency with built-in memory and tool integration.

Mental Models & Methodologies

Task Decomposition FrameworkA/B Testing & Evaluation MetricsPrompt Template Versioning (like Git for prompts)

Task decomposition breaks problems into CoT steps. Rigorous A/B testing with metrics (e.g., F1-score for classification, BLEU for summary) is non-negotiable for evidence-based prompt optimization. Versioning tracks iterative improvements.

Interview Questions

Answer Strategy

The interviewer is testing systematic design thinking. Structure the answer by mapping problem complexity to techniques. Sample answer: 'I'd use a hybrid approach. First, a Chain-of-Thought prompt guides the model to analyze the stack trace line-by-line. Then, I'd implement a ReAct pattern: the Thought identifies potential causes, the Action searches the codebase or documentation for relevant functions, and the Observation provides the context. For critical fixes, I might run this through self-consistency with multiple CoT variations to converge on the most reliable diagnosis.'

Answer Strategy

This tests problem-solving and reflective learning. Focus on the failure mode and the technical pivot. Sample answer: 'Initially, I used a simple zero-shot prompt for extracting structured data from legal contracts, but it hallucinated clause references. I diagnosed this as a lack of grounding. The fix was a two-step ReAct approach: first, the model was instructed to 'search' and quote the exact relevant text (Action), then reason over that extracted text to generate the final output. This grounded the model, reduced hallucinations by over 40%, and taught me the critical importance of providing verifiable anchors for the model's reasoning.'