AI Engineering Roadmap
A comprehensive roadmap outlining the key skills needed to become a successful AI engineer, focusing on practical application and industry best practices. This roadmap incorporates principles of spaced repetition, interleaving, and feedback mechanisms to foster long-term retention and skill mastery. It addresses potential psychological barriers to learning and incorporates social learning aspects for enhanced motivation and engagement.
Program Modules
Working with LLMs
Understand different LLM providers (OpenAI, Anthropic, Meta, Google), their APIs, and various model types. Learn about streaming, batch processing, and prompt caching. Explore running your own models using platforms like OpenRouter or Ollama. This activity incorporates spaced repetition and interleaving techniques for improved learning.
Understanding LLM Personalities
DailyLearn how different LLMs have different strengths (e.g., OpenAI for analysis, Anthropic for writing, Gemini for search).
“We found that the OpenAI models tend to be the best analysts, the Anthropic models tend to be the best writers, and the Gemini models broadly tend to be the best detectives.”
Working with LLM APIs
DailyFamiliarize yourself with common API calls (e.g., openai.Completion.create) and concepts like streaming, batch processing, prompt caching.
Local vs. Open Source Models
WeeklyExplore running your own models using platforms like OpenRouter or Ollama.
Prompt Engineering
Master the art of prompting to elicit desired behaviors from LLMs. Learn techniques like Chain of Thought, providing examples, and using structured outputs. This activity includes examples and structured exercises to reinforce learning.
Chain of Thought Prompting
DailyPractice prompting LLMs to explain their reasoning process before providing an answer.
Example-Based Prompting
DailyLearn how to use examples to guide the LLM's response.
Structured Output Prompting
DailyPractice prompting for structured outputs (JSON, tables) for easier data processing.
Retrieval Augmented Generation (RAG)
Learn to combine LLMs with external knowledge sources for enhanced context awareness. This includes activities on embeddings and semantic search.
Retrieval Augmented Generation (RAG)
WeeklyLearn to combine LLMs with external knowledge sources for enhanced context awareness. This includes activities on embeddings and semantic search.
LLM Orchestration
Learn to build systems that combine multiple LLMs and tools. Explore frameworks like LangChain and the concept of Agents.
LLM Orchestration
WeeklyLearn to build systems that combine multiple LLMs and tools. Explore frameworks like LangChain and the concept of Agents.
Evaluation and Observability
Develop robust evaluation and monitoring strategies for LLM applications. This includes cost management and performance tracking.
Evaluation and Observability
WeeklyDevelop robust evaluation and monitoring strategies for LLM applications. This includes cost management and performance tracking.
AI Engineering Mindset
Develop a mindset for building with AI, focusing on iterative development, rapid prototyping, and understanding the evolving tool stack. Includes strategies for overcoming procrastination and building self-compassion.
Build First, Build Quickly
DailyEmbrace the iterative process and prioritize rapid prototyping.
Overcoming Procrastination
WeeklyImplement techniques to overcome procrastination and maintain momentum.
Self-Compassion Exercises
WeeklyPractice self-compassion to manage anxiety and build resilience.
What You'll Accomplish
- Understand the core principles of Large Language Models (LLMs).
- Master prompt engineering techniques for effective LLM interaction.
- Implement retrieval-augmented generation (RAG) for enhanced context awareness.
- Design and build LLM-powered applications using orchestration frameworks.
- Develop robust evaluation and observability strategies for LLM applications.
- Cultivate a mindset conducive to building and iterating on AI-powered projects.
Full program access + updates