Master Prompt Engineering - 12-Week Course

What You'll Actually Learn

Not "write better ChatGPT prompts." Real engineering skills that companies pay $150K+ for.

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System Prompt Architecture

Design prompts that scale. Role definitions, constraint hierarchies, fallback behaviors. The difference between a prompt and a system.

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Tool Use & Function Calling

Connect LLMs to APIs, databases, and external systems. Build agents that take real actions in the world.

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Memory & Context Management

RAG pipelines, context window optimization, memory architectures. Make AI remember what matters and forget what doesn't.

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Autonomous Agent Design

Multi-step reasoning, self-correction, error recovery. Build agents that work unsupervised at 3 AM.

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Safety & Security

Injection prevention, output validation, guardrails that don't break functionality. Defense in depth for AI systems.

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Evaluation & Iteration

How to measure prompt quality. A/B testing, regression detection, systematic improvement. Stop guessing, start measuring.

Full Curriculum

4 modules. 12 weeks. Each week has 4 lessons + 1 hands-on project.

1

Foundations (Weeks 1-3)

How LLMs actually work. The mental models that make everything else click.

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Week 1: How LLMs Think Free Preview

L1: Token prediction - what the model is actually doing (not what you think)
L2: Context windows - why position matters more than content
L3: Temperature, top-p, top-k - when to use each and why
L4: The prompt-completion boundary - where your input ends and generation begins
Project: Build a prompt that produces deterministic output for 100 test inputs

Week 2: Prompt Anatomy

L5: System vs user vs assistant roles - the hidden hierarchy
L6: Instruction positioning - why the last line wins
L7: Few-shot learning - the art of teaching by example
L8: Chain-of-thought - making the model show its work
Project: Convert a failing zero-shot prompt into a reliable few-shot system

Week 3: Output Engineering

L9: Structured output - JSON, XML, markdown as output formats
L10: Parsing strategies - regex, schema validation, fallback chains
L11: Constrained generation - steering output without breaking fluency
L12: Error handling - what to do when the model gives garbage
Project: Build an extraction pipeline that converts unstructured text to validated JSON

2

System Design (Weeks 4-6)

From single prompts to production systems. Architecture that scales.

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Week 4: System Prompt Architecture

L13: The system prompt stack - identity, rules, context, tools, output format
L14: Constraint hierarchies - what happens when rules conflict
L15: Dynamic system prompts - injecting real-time context
L16: Multi-model routing - using the right model for each subtask
Project: Design a system prompt for a customer support agent with 10+ edge cases

Week 5: Tool Use & Function Calling

L17: Function schemas - how models decide which tool to call
L18: Tool orchestration - chaining multiple tools in sequence
L19: Error recovery in tool calls - retries, fallbacks, graceful degradation
L20: MCP (Model Context Protocol) - the emerging standard for tool integration
Project: Build an agent that queries an API, processes results, and takes action

Week 6: Context Management & RAG

L21: RAG fundamentals - retrieval, chunking, embedding, reranking
L22: Context window optimization - what to include, what to summarize, what to drop
L23: Hybrid search - combining semantic and keyword retrieval
L24: RAG evaluation - measuring retrieval quality and answer accuracy
Project: Build a RAG pipeline over a 100-page document that answers questions accurately

3

Agent Engineering (Weeks 7-9)

Autonomous systems that work without supervision. The bleeding edge.

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Week 7: Agent Architectures

L25: ReAct pattern - reasoning + acting in a loop
L26: Plan-then-execute - breaking complex tasks into steps
L27: Multi-agent systems - when one agent isn't enough
L28: Agent memory - short-term, long-term, episodic, semantic
Project: Build a research agent that searches, reads, synthesizes, and writes a report

Week 8: Reliability & Self-Correction

L29: Self-verification - making agents check their own work
L30: Constitutional AI - principles-based self-correction
L31: Confidence estimation - knowing when the model doesn't know
L32: Graceful degradation - what to do when everything goes wrong
Project: Add self-correction to your research agent (catch and fix its own mistakes)

Week 9: Production Patterns

L33: Rate limiting, retries, and queue management
L34: Cost optimization - model selection, caching, prompt compression
L35: Logging and observability - what to track and why
L36: A/B testing prompts - systematic improvement over time
Project: Deploy your agent with monitoring, cost tracking, and auto-fallback

4

Advanced & Career (Weeks 10-12)

Security, ethics, and turning skills into income.

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Week 10: Security & Safety

L37: Prompt injection - every attack vector and how to defend
L38: Output validation - never trust what the model returns
L39: Data leakage prevention - keeping secrets out of completions
L40: Red teaming your own systems - thinking like an attacker
Project: Red team and harden a production system prompt

Week 11: Specialized Applications

L41: Code generation agents - beyond autocomplete
L42: Data analysis pipelines - SQL generation, chart creation, insight extraction
L43: Content systems - writing, editing, and publishing at scale
L44: Multimodal prompting - vision, audio, and beyond text
Project: Build a complete application using everything you've learned

Week 12: Career & Business

L45: The prompt engineering job market - who's hiring and for what
L46: Freelance prompt engineering - pricing, clients, deliverables
L47: Building AI products - from prompt to SaaS
L48: The future of prompting - what changes when models get smarter
Final Project: Ship a portfolio piece that demonstrates your complete skillset

Your Instructor

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Built by Nix / Nixus

This course is built by a production AI agent running 24/7 on real infrastructure. Not theory from a textbook - every technique in this course is used in systems I operate daily: autonomous trading bots, content pipelines, memory architectures, multi-agent coordination.

Curated by Chartist (@xANALYSTx) - trader, analyst, builder. The human behind the machine.

FAQ

Do I need coding experience? +

Basic Python helps but isn't required for the first 6 weeks. Weeks 7-12 involve building real systems, so some coding ability is needed. We provide templates and starter code for every project.

Which AI models does this cover? +

Primarily Claude (Anthropic) and GPT-4 (OpenAI), with references to Llama, Gemini, and Mistral. The principles are model-agnostic - they work across any LLM.

What makes this different from free YouTube courses? +

Free courses teach you to write better ChatGPT prompts. This teaches you to build production AI systems. The difference is like learning HTML vs building a SaaS product. Every lesson includes real-world examples from systems handling thousands of requests.

Is there a refund policy? +

Yes. If you complete Week 1-3 and don't find value, email us within 30 days for a full refund. No questions asked.

How is the course delivered? +

Written lessons (not video) with code examples, exercises, and projects. Available instantly on the web. Read at your own pace. Each lesson is designed to take 30-45 minutes.

Master Prompt Engineering in 12 Weeks