Guide to AI Engineering

If you’re serious about becoming an AI engineer—especially one who builds LLM-based applications and agents—then this is your study-and-build roadmap (CREDIT: AIMakerspace). These are the essential topics to master. But don’t just read or watch—build something small for each. That’s how you truly internalize complex systems.

• ✅ Search online for resources (docs, videos, blogs)
• 🛠️ Build a project or demo
• 📈 Reflect and evaluate your results

1. Embeddings and Retrieval Augment Generation (RAG)

• Prompt Engineering best practices
• Overview of the LLM App Stack
• Understand embedding models and similarity search
• Understand Retrieval Augmented Generation = Dense Vector Retrieval + In-Context Learning
• Build a Python RAG app from scratch

2. Industry Use Cases & End-to-End RAG

• The state of production LLM application use cases in industry
• Build an end-to-end RAG application

3. Production-Grade RAG with LangGraph

• Why LangChain, OpenAI, QDrant, LangSmith
• Understand LangChain & LangGraph core constructs
• Understand (enough) LangGraph and LangSmith
• Build a RAG system with LangChain and Qdrant
• How to use LangSmith for evaluation and monitoring tool for your RAG application

4. Production-Grade Agents with LangGraph

• Answer the question: “What is an agent?”
• Understand how to build production-grade agent applications using LangGraph
• How to use LangSmith to evaluate more complex agentic RAG applications

5. Multi-Agent Applications

• Understand what multi-agent systems are and how they operate
• Build a production-grade multi-agent applications using LangGraph

6. Synthetic Data Generation for Evaluation

• An overview of Synthetic Data Generation (SDG)
• How to use SDG for Evaluation
• Generating high-quality synthetic test data sets for RAG applications
• How to use LangSmith to baseline performance, make improvements, and then compare

7. RAG and Agent Evaluation

• Build RAG and Agent applications with LangGraph
• Evaluate RAG and Agent applications quantitatively with the RAG ASsessment (RAGAS) framework
• Use metrics-driven development to improve agentic applications, measurably, with RAGAS

8. Advanced Retrieval Strategies for RAG Apps

• Understand how advanced retrieval and chunking techniques can enhance RAG
• Compare the performance of retrieval algorithms for RAG
• Understand the fine lines between chunking, retrieval, and ranking
• Learn best practices for retrieval pipelines

9. Advanced Agentic Reasoning

• Discuss best-practice use of reasoning models
• Understand planning and reflection agents
• Build an Open-Source Deep Research agent application using LangGraph
• Investigate evaluating complex agent applications with the latest tools

10. OpenAI Agents SDK

• Understand the suite of tools for building agents with OpenAI and the evolution of their tooling
• Core constructs of the Agents SDK and comparison to other agent frameworks
• How to use monitoring and observability tools on the OpenAI platform

11. Contextual Retrieval

• Understand how Contextual Retrieval = Contextual Embeddings + Contextual BM25 works, and how it differs from traditional and hybrid RAG approaches
• Discuss the pros and cons of using a metadata filtering approach vs. Contextual Retrieval
• Build a Contextual Retrieval application and test its performance on different data

12. Code Agents, Coding Agents, and Computer Use Agents

• Defining code agents, coding agents, and computer use agents
• Understand the suite of tools for building code agents with Hugging Face’s Smol Agents library
• Understand the landscape of coding and computer use agents

13. Production Endpoints

• Discuss the important production-ready capabilities of LangChain under the hood
• Understand how to deploy open LLMs and embeddings to scalable endpoints
• Discuss how to choose inference server
• Build an enterprise RAG application with LCEL

14. Deploying Applications to APIs and LLM Ops

• Defining LLM Operations (LLM Ops)
• Learning how to monitor, visualize, debug, and interact with your LLM applications with LangSmith and LangGraph Studio
• Deploy your applications to APIs directly via LangGraph Platform

15. On-Prem RAG and Agent Applications

• Introduction to Building On-Prem
• Hardware & compute Considerations
• Local LLM & Embedding Model Hosting Comparison
• How to build and present an On-Prem Solution to stakeholders

16. Caching, Versioning, Guardrails, Protocols

• How to use Prompt caching
• How to build/iterate on version-controlled Prompt and Tool Libraries for your engineering team
• Introduction to Model Context Protocol (MCP) and Agent2Agent (A2A) Protocols
• The role of MCP servers: inside vs. outside the enterprise, developer vs. consumer products

💡 Final Tip: Thoroughly go through this list like your personal AI engineering syllabus. Whether you're aiming for OpenAI, LangChain, or your own LLM product—mastering this gives you a serious edge.