Learning Roadmap
How to Become a Prompt Systems Designer
A step-by-step, phase-based learning path from beginner to job-ready Prompt Systems Designer. Estimated completion: 6 months across 4 phases.
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Foundations: LLMs & Basic Prompting
4 weeksGoals
- Understand core transformer concepts and LLM text generation
- Master basic prompt patterns (zero-shot, few-shot, instruction-following)
- Learn to use the OpenAI and Anthropic APIs for simple tasks
Resources
- Anthropic's 'Introduction to Prompt Engineering' documentation
- OpenAI's 'GPT Best Practices' guide
- Fast.ai 'Practical Deep Learning for Coders' (NLP sections)
MilestoneCan design and implement effective basic prompts for classification, summarization, and Q&A tasks via API.
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System Engineering: Chains & RAG
6 weeksGoals
- Learn to design and build prompt chains using LangChain
- Implement a basic Retrieval-Augmented Generation (RAG) system
- Understand structured data output (JSON mode, function calling)
Resources
- LangChain documentation and tutorial series
- DeepLearning.AI 'LangChain for LLM Application Development' course
- Pinecone or Weaviate 'RAG 101' learning resources
MilestoneCan architect and prototype a multi-step RAG system that answers questions from a custom knowledge base.
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Advanced Systems: Agents, Evaluation & Safety
8 weeksGoals
- Design agent systems with tool use and planning capabilities
- Build rigorous evaluation frameworks for prompt systems
- Implement safety guardrails and content moderation layers
Resources
- DeepLearning.AI 'AI Agents in LangGraph' course
- LangSmith documentation for tracing and evaluation
- Research papers on LLM evaluation (e.g., 'Chatbot Arena') and safety
MilestoneCan design an agent with custom tools, write comprehensive eval suites, and deploy guardrails to block harmful outputs.
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Productionization & Specialization
6 weeksGoals
- Learn CI/CD for prompt systems and versioning strategies
- Explore advanced optimization (DSPy, prompt tuning)
- Develop domain-specific expertise (e.g., legal, coding, healthcare)
Resources
- DSPy documentation for optimizing LM pipelines
- MLOps resources for prompt management at scale
- Case studies from companies like Stripe, Duolingo, or Morgan Stanley on LLM integration
MilestoneCan manage prompt systems as production-grade software, optimize them programmatically, and apply deep expertise to a vertical domain.
Practice Projects
Apply your skills with hands-on projects. Ordered by difficulty.
Multi-Modal Research Assistant
AdvancedBuild a system that accepts a PDF research paper (as text) and a voice query (transcribed). It should summarize the paper, answer questions about it, and extract key figures/tables, all while citing specific sections.
Resilient Customer Support Agent
IntermediateCreate an agent that handles support tickets for a fictional e-commerce site. It must follow a strict policy, handle angry users, escalate appropriately, and resist basic prompt injection attempts.
Prompt-as-Code DevOps Pipeline
IntermediateSet up a Git repository where prompts are versioned YAML/JSON files. Create a GitHub Action that, on commit, runs an evaluation suite (using a small dataset) and posts the results as a PR comment.
Domain-Specific Code Review Bot
AdvancedDesign a prompt system that takes a code diff and provides insightful review comments based on a given coding style guide (e.g., Google's Python style). It should explain why changes are suggested.
Interactive Story Generator with Consistent Memory
BeginnerBuild a text-based game where the AI narrator remembers character names, plot points, and world details from previous interactions, maintaining consistency over a long session.
Ready to Start Your Journey?
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