Prompt Engineering - Expanded Project Guides

Prompt Engineering - Expanded Project Guides

Generated from: PROMPT_ENGINEERING_PROJECTS.md

This folder contains deep-dive guides for each project in the Prompt Engineering learning path.

Overview

This learning journey transforms prompt engineering from an art into a rigorous engineering discipline. You’ll move beyond “prompt whispering” to build production-grade systems with schemas, tests, version control, and reliability guarantees.

Core Philosophy: Treat prompts as software artifacts—functions with defined inputs, invariant constraints, and strictly typed outputs.

Why This Matters

  • Reliability: 80% accuracy is a broken feature. Production systems need 99.9% reliability.
  • Security: Prompt injection is the SQL Injection of the AI era. Learn to structure data boundaries.
  • Scale: Manual review doesn’t scale. Build automated evaluation pipelines.
  • Cost: Context windows are expensive. Master efficient context management.

Project Index

# Project Difficulty Time Key Focus
1 Prompt Contract Harness Intermediate 3-5 days Testing & Invariants
2 JSON Output Enforcer Advanced 4-6 days Schema Validation & Repair
3 Prompt Injection Red-Team Lab Advanced 5-7 days Security & Instruction Hierarchy
4 Context Window Manager Advanced 5-7 days RAG & Context Optimization
5 Few-Shot Example Curator Intermediate 4-6 days Example Selection & Clustering
6 Tool Router Advanced 6-8 days Function Calling & Intent Classification
7 Temperature Sweeper Advanced 5-7 days Reliability Curves & Uncertainty

Learning Paths

Path 1: Foundation (Start Here)

Goal: Build testing and validation infrastructure

  1. Project 1: Prompt Contract Harness - Learn to test prompts like code
  2. Project 2: JSON Output Enforcer - Master structured outputs and error recovery
  3. Project 5: Few-Shot Example Curator - Understand example selection strategies

Path 2: Security & Reliability

Goal: Build production-hardened systems

  1. Project 3: Prompt Injection Red-Team Lab - Master security boundaries
  2. Project 7: Temperature Sweeper - Understand reliability vs. creativity tradeoffs
  3. Project 1: Prompt Contract Harness - Apply security testing patterns

Path 3: Production Systems

Goal: Build scalable, cost-efficient systems

  1. Project 4: Context Window Manager - Master RAG and context optimization
  2. Project 6: Tool Router - Build agentic systems with function calling
  3. Project 7: Temperature Sweeper - Implement confidence-based policies

Core Concepts Covered

Concept Description Primary Projects
Prompt Contracts Prompts as functions with defined I/O P1, P2
Instruction Hierarchy System vs. user vs. data message boundaries P3
Data Grounding Preventing hallucination through context P4
Structured Outputs JSON/XML schemas as API surfaces P2, P6
Evaluation (Evals) Automated testing for prompt regression P1, P7
Context Management Token budgets and relevance scoring P4
Tool Routing Function schema design and intent classification P6
Uncertainty Quantification Temperature, sampling, and confidence P7

Prerequisites

Required Knowledge:

  • Programming fundamentals (Python or TypeScript)
  • Basic understanding of APIs and JSON
  • Familiarity with command-line tools

Required Tools:

  • Python 3.8+ or Node.js 16+
  • API key for OpenAI or Anthropic
  • Git for version control
  • Text editor or IDE

Recommended Background:

  • Experience with unit testing frameworks
  • Understanding of REST APIs
  • Basic knowledge of regex and string manipulation

Skill Progression

Beginner
   │
   ├─► Project 1: Testing fundamentals
   │
Intermediate
   │
   ├─► Project 5: Example engineering
   ├─► Project 2: Schema validation
   │
Advanced
   │
   ├─► Project 3: Security hardening
   ├─► Project 4: Context optimization
   ├─► Project 6: Agentic systems
   └─► Project 7: Reliability engineering

Real-World Applications

These projects prepare you to build:

  • Customer Support Bots: Grounded, schema-validated responses with citation
  • Data Extraction Pipelines: Reliable structured output from unstructured text
  • Security-Hardened Chatbots: Injection-resistant systems with proper boundaries
  • RAG Systems: Efficient document retrieval and context management
  • Agentic Workflows: Multi-tool systems with reliable function routing
  • Production LLM Apps: Monitored, tested, and reliability-engineered systems

Success Criteria

By completing this learning path, you will:

  1. Understand the fundamental contracts governing LLM behavior
  2. Build production-grade evaluation and testing infrastructure
  3. Implement security boundaries resistant to prompt injection
  4. Optimize context windows for cost and performance
  5. Design reliable systems with measurable SLOs
  6. Deploy agentic systems with function calling and tool use

Essential

  • “Prompt Engineering Guide” (promptingguide.ai) - Techniques section
  • “OWASP Top 10 for LLMs” - Security fundamentals
  • “Building LLM Applications for Production” by Chip Huyen - Evaluation patterns

Advanced

  • “Designing Data-Intensive Applications” by Martin Kleppmann - Schema design
  • “Site Reliability Engineering” by Google - SLO thinking
  • “Release It!” by Michael T. Nygard - Stability patterns

Getting Started

  1. Choose your path from the learning paths above
  2. Read the project guide for your first project
  3. Study the theoretical foundation section before coding
  4. Build iteratively following the implementation phases
  5. Complete the self-assessment before moving to the next project

Project File Structure

Each project guide includes:

  • Learning Objectives: What you’ll master
  • Theoretical Foundation: Core concepts and background
  • Project Specification: What you’ll build
  • Solution Architecture: High-level design approach
  • Implementation Guide: Phase-by-phase development
  • Testing Strategy: How to verify correctness
  • Common Pitfalls: Debugging and troubleshooting
  • Extensions: Ways to go deeper
  • Resources: Books, papers, and tools

For the complete introduction and concept analysis, see PROMPT_ENGINEERING_PROJECTS.md