🤖 AI & Agentic AIBeginner LevelEvidence-Based Skill Profile
Hands-on Mastery in Prompt Engineering
Don't just prompt. Master the science and engineering of systematic, reliable LLM instruction design. Master prompt anatomy, few-shot exemplar curation, chain-of-thought architectures, strict JSON/Pydantic schema enforcement, prompt decomposition, automated evaluation harnesses, and defensive security guardrails with your dedicated in-course Prompt Engineering Mastery Agent coach.
14 Modules • 7 Production Projects
14 Modules
AI FearFilter Faculty
What You Will Learn
Architect deterministic, production-grade prompt templates using the RTFCC (Role, Task, Context, Constraints, Format) framework.
Curate high-impact few-shot exemplars that drastically boost model reliability and reduce edge-case errors.
Implement multi-step reasoning patterns including zero-shot CoT, least-to-most prompting, and self-consistency voting.
Guarantee 100% parseable structured outputs (JSON, Pydantic, Markdown) with grammar-constrained decoding and retry loops.
Decompose complex multi-stage tasks into modular prompt pipelines and tool-augmented workflows.
Establish automated prompt evaluation pipelines with continuous regression testing and rubric-based LLM-as-a-judge scoring.
Harden prompt endpoints against indirect prompt injections, jailbreaks, and system prompt leakage with defensive guardrails.
Complete 7 production-grade projects and successfully pass the Enterprise Capstone Challenge evaluated by the Prompt Engineering Mastery Agent.
Curriculum & Weekly Roadmap
14 Structured ModulesModule 1 — Introduction to Prompt Engineering
- What Prompt Engineering Truly Is (and Isn't)
- Why Prompts Matter in Production LLM Applications
- Prompt Anatomy: Instructions, Context, Constraints & Output Formats
Module 2 — Prompt Fundamentals & In-Context Learning
- Zero-Shot vs Few-Shot Prompting Foundations
- Role Prompting & System Persona Shaping
- Context Injection, Delimiters (XML/Markdown) & Clear Task Specifications
Module 3 — Prompt Design Patterns & Reasoning Architectures
- Structured Prompting Frameworks (RTFCC & CRISP)
- Chain-of-Thought (CoT) & Step-by-Step Reasoning Guidance
- Prompt Decomposition & Iterative Refinement Workflows
Module 4 — Context Management & Token Optimization
- Context Window Budgeting & The "Lost in the Middle" Effect
- Dynamic Context Insertion & Prompt Compression Techniques
- Token Efficiency & Cost Optimization for Production Prompts
Module 5 — Structured Outputs & Schema Enforcement
- Deterministic JSON Output Generation & Schema Anchoring
- Pydantic Validation & Robust Fallback Retries
- Parsing Reliability: Handling Model Hallucinations in Strict Schemas
Module 6 — Advanced Prompting Techniques
- Self-Consistency Decoding & Majority Voting
- Generated Knowledge Prompting & Directional Stimulus
- Least-to-Most & Tree-of-Thoughts Reasoning Frameworks
Module 7 — Defensive Prompt Engineering & Guardrails
- Treating Input Strictly as DATA: Defending Against Direct & Indirect Injections
- Jailbreak Mitigation & System Prompt Confidentiality Protection
- Input/Output Guardrail Layers (Regex, Classification, Moderation APIs)
Module 8 — Tool Use & Function Calling Prompts
- Prompt Architecture for Tool Selection & Parameter Extraction
- Handling Ambiguous Tool Invocations & Error Rectification Prompts
- Multi-Tool Coordination in ReAct Loops
Module 9 — Prompt Evaluation & Testing Pipelines
- Creating High-Coverage Test Datasets for Prompt Benchmarking
- Deterministic vs LLM-as-a-Judge Evaluation Metrics
- A/B Testing, Regression Detection & Prompt CI/CD Pipelines
Module 10 — Prompt Optimization & Metaprompting
- Automatic Prompt Engineering (APE) & Optimization Algorithms
- DSPy Concepts: Compiling Declarative Signatures into Optimized Prompts
- Metaprompting: Using Foundation Models to Write and Refine Prompts
Module 11 — Domain-Specific Prompt Architectures
- Engineering Prompts for Code Generation & Analysis
- Data Extraction, Schema Normalization & Document Intelligence
- Customer Support, Reasoning Memos & Domain Verification Prompts
Module 12 — Production Prompt Management & Versioning
- Prompt Versioning, Registries & Deployment Strategies
- Observability: Tracing Prompt Latency, Token Drift & Cost Attribution
- CI/CD Workflows for Updating Production Prompt Assets
Module 13 — Building Production Prompt Systems (7 Practical Projects)
- Projects 1-3: Structured Extraction Engine, Reasoning Pipeline, Defensive Guardrail Gateway
- Projects 4-5: Multi-Tool Function Calling Assistant & Automated Prompt Eval Benchmarking Suite
- Projects 6-7: Dynamic Metaprompt Optimizer & Production Multi-Stage Enterprise Prompt System
Module 14 — Final Capstone Prompt Engine Challenge
- Enterprise Multi-Stage Prompt Engine Architecture Specifications
- Implementation: Role Definition, Few-Shot Curation, JSON Enforcement & Guardrails
- Submission, Multi-Criteria Evaluation & Prompt Engineering Mastery Agent Verification
Who This Course Is For
Software Engineers, AI Practitioners, Technical Product Managers, Data Scientists, and Developers seeking to build rock-solid, production-grade LLM applications with deterministic and secure prompt systems.
Key Skills Developed:
Prompt Anatomy & Instruction EngineeringFew-Shot In-Context Exemplar CurationChain-of-Thought & Reasoning ArchitecturesStructured Output Schemas (JSON/Pydantic)Prompt Decomposition & Complex Task OrchestrationAutomated Evaluation & Continuous Prompt OptimizationDefensive Guardrails & Injection ResilienceEnterprise Capstone Prompt Engine Challenge
Course Faculty & Development
AI FearFilter Faculty
Prompt Systems & Evaluation Engineering Team
AI FearFilter Academy
Engineering CurriculumAI FearFilter Academy
100% FREEFree For All Students
100% Self-Paced + Active Hands-on Learning
Evidence-Based Demonstrated Skill Profile
Full Lifetime Access in Student Home
FILTER FEAR. TRUST FACTS.