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01 AI and LLM Foundations Understand how LLMs are built and why they behave the way they do before you start prompting. 02 Core Prompt Engineering Principles Write clear, well-structured prompts and refine them systematically based on model output. 03 Advanced Reasoning Strategies Improve accuracy on complex tasks with structured reasoning, decomposition, and self-critique. 04 LLM APIs and Frameworks Call models programmatically, tune generation parameters, and build with orchestration frameworks. 05 Context Engineering and RAG Give models the right information at the right time with retrieval, long context, and memory. 06 Tool Use, Structured Outputs, and Agents Connect LLMs to tools and data, enforce structured outputs, and design reliable agent loops. 07 Evaluation and Prompt Optimization Measure prompt quality objectively and optimize prompts with data-driven and automated methods. 08 Domain-Specific Prompt Engineering Apply prompting skills to coding, content, data, synthetic data, and multimodal tasks. 09 Production, Security, and Governance Ship prompts safely with observability, versioning, attack defenses, and compliance controls. 10 Capstone Projects and Career Build a portfolio of real prompt-driven products and prepare for prompt engineering roles. Prompt Engineer For developers, analysts, and builders who want reliable results from LLMs. You will design, evaluate, and ship prompts, RAG pipelines, and agents that work in production. 10 STAGES · 37 TOPICS · 135 SUBTOPICS 1.1 Introduction to AI and Machine Learning History and Evolution of AI Learning Paradigms Supervised · Unsupervised · Reinforcement Deep Learning Basics 1.2 NLP Essentials Tokenization Tokens · Subwords · Context Windows Embeddings and Vector Spaces Attention and the Transformer 1.3 How LLMs Work Pre-training vs Fine-tuning Autoregressive Next-Token Generation Scaling Laws and Model Sizes Limitations Hallucinations · Knowledge Cutoff · Bias 1.4 The Model Landscape Frontier Models GPT · Claude · Gemini Open-Weight Models Llama · Qwen · Mistral · DeepSeek · Gemma Reasoning Models Extended Thinking · Test-Time Compute Multimodal LLMs Vision · Audio · Video 2.1 Anatomy of a Prompt Prompt Components Instructions · Context · Input Data · Output Indicator Message Roles System · User · Assistant Clarity and Specificity Delimiters and Formatting Markdown · XML Tags 2.2 Basic Prompting Techniques Zero-Shot Prompting Few-Shot Prompting Role and Persona Prompting Prompt Templates and Variables Meta-Prompting 2.3 Controlling Outputs Output Formats JSON · XML · YAML · Markdown Length, Tone, and Style Constraints Negative Prompting and Avoidance 2.4 Iterative Refinement Analyzing Model Responses Debugging Failing Prompts A/B Testing Prompts 3.1 Reasoning Techniques Chain of Thought Zero-Shot CoT · Few-Shot CoT Self-Consistency Tree and Graph of Thoughts ToT · GoT Step-Back Prompting Directional Stimulus Prompting 3.2 Decomposition and Planning Plan-and-Solve Prompting Least-to-Most Prompting Prompt Chaining 3.3 Self-Critique and Verification Self-Reflection and Self-Correction Chain-of-Verification (CoVe) 3.4 Prompting Reasoning Models When to Use Reasoning Models Reasoning Effort and Thinking Budgets Minimal Prompts for Built-In Reasoning 4.1 LLM Provider APIs Commercial APIs OpenAI · Anthropic · Google Gemini Open-Weight Models Hugging Face · Ollama · vLLM API Keys, Rate Limits, and Pricing 4.2 Generation Parameters Temperature, Top-P, and Top-K Frequency and Presence Penalties Max Tokens and Stop Sequences Seeds and Reproducibility 4.3 Orchestration Frameworks LangChain LlamaIndex Haystack Vercel AI SDK 5.1 Context Engineering Basics Context Window Budgeting Conversation Memory and State Context Compression and Summarization 5.2 Retrieval-Augmented Generation RAG Fundamentals Retrieve · Augment · Generate Chunking Strategies Embeddings and Semantic Search Hybrid Search and Reranking Advanced RAG GraphRAG · Self-RAG · Corrective RAG 5.3 Long-Context Prompting Document Placement and Ordering Needle-in-a-Haystack Testing Prompt Caching 6.1 Function Calling How Function Calling Works Designing Tool Descriptions Error Handling and Fallbacks Model Context Protocol (MCP) 6.2 Structured Outputs JSON Schema Constraints Schema Validation Pydantic · Zod Retry and Repair Strategies 6.3 Agentic Patterns ReAct (Reasoning and Acting) Planning and Tool Selection Multi-Agent Collaboration and Debate Human-in-the-Loop Checkpoints 6.4 Agent Frameworks LangGraph CrewAI OpenAI Agents SDK Microsoft Agent Framework 7.1 Evaluation Fundamentals Golden Datasets and Ground Truth Quantitative vs Qualitative Evaluation LLM-as-a-Judge Human Review and Rubrics 7.2 Metrics and Tools Reference Metrics Exact Match · BLEU · ROUGE · BERTScore Semantic Similarity Evaluation Frameworks Ragas · DeepEval · promptfoo · OpenAI Evals Regression Testing for Prompts 7.3 Automated Prompt Engineering Automatic Prompt Engineer (APE) Optimization by Prompting (OPRO) DSPy and Declarative Prompting Prompt Optimizers TextGrad · Evolutionary Search 8.1 Coding and Software Development Code Generation and Explanation Code Review, Refactoring, and Tests AI Coding Tools GitHub Copilot · Cursor · Claude Code 8.2 Content Creation and Copywriting Brand Voice and SEO Copy Creative Writing and Ideation Persona-Driven Copywriting 8.3 Data Analysis and Extraction Entity Extraction from Text Summarization Techniques Natural Language to SQL (NL2SQL) 8.4 Synthetic Data Generation Generating High-Quality Datasets Data Augmentation for Fine-Tuning Distillation Pipelines 8.5 Multimodal Prompting Text-to-Image Midjourney · GPT Image · Stable Diffusion · FLUX Text-to-Video Veo · Sora · Runway Audio and Speech Prompts Visual Question Answering (VQA) 9.1 LLMOps for Prompts Prompt Versioning and Management Tracing and Observability LangSmith · Langfuse · Helicone · Phoenix Semantic Caching Model Routing and Fallbacks LiteLLM · OpenRouter Cost and Latency Tracking 9.2 Prompt Injection and Security Prompt Injection Direct · Indirect Jailbreaking and Defenses Defensive Prompt Design Data Poisoning Awareness 9.3 Bias, Fairness, and Alignment Identifying Model Bias Mitigating Bias via Prompts Alignment Methods RLHF · RLAIF · Constitutional AI 9.4 Privacy, Compliance, and Red Teaming PII and PHI Masking Copyright and Legal Considerations AI Regulations EU AI Act · GDPR Red Teaming LLM Apps garak · PyRIT · promptfoo 10.1 Capstone Projects Prompt Library with Evaluation Suite Production RAG Chatbot Tool-Using Agent with MCP Structured Data Extraction Pipeline 10.2 Portfolio and Visibility Documented Prompt Case Studies Before-and-After Evaluation Reports Open-Source Contributions Technical Blog Posts 10.3 Interview Preparation Live Prompt Design Exercises Evaluation and Debugging Scenarios LLM Application System Design Safety and Risk Discussions