Be an AI Engineer in 2026. Learn this:
1. The Vector Stack:
- Embeddings (Text-to-Vector)
- Vector Databases (Storage)
- Semantic vs. Hybrid Search
2. RAG & Context:
- Advanced Retrieval (Reranking)
- GraphRAG (Knowledge Graphs + Vectors)
- Long-context window management
3. Pipelines & Orchestration:
- Chaining (Deterministic flows)
- Routing (Selecting the right model for the task)
- Frameworks: LangChain / LlamaIndex
4. The Agentic Layer:
- Tool Use (Search, APIs, Code Interpreters)
- Planning Loops (Reasoning before acting)
- Multi-Agent Orchestration (Swarms)
5. Evaluation & Testing:
- Golden Datasets (Establishing Ground Truth)
- LLM-as-a-Judge (Using strong models to grade weak ones)
- Metrics: Faithfulness, Answer Relevance, Recall
- Continuous Eval in CI/CD pipelines
6. Ops & Monitoring:
- Tracing (Debugging the chain)
- Cost & Latency optimization
Want help? Then attend this:
🚨 Want to learn how to build + ship AI and Data Science projects (that businesses actually want in 2026)?
On September 23rd, I am hosting a free workshop to help you get started with AI + DS projects in Python (free).
Register here (500 seats): https://t.co/onpLpRwkzH
Andrew Ng just released a 2-hour course on full Graph Engineering.
How to go from one prompt to 100 agents that loop, improve themselves, and run without you:
0% → 09:14 - build your first AI agent
25% → 33:11 - run agents with loop engineering
50% → 1:02:46 - turn agent loops into graphs
75% → 1:30:15 - build agents that rewrite themselves
100% → 1:49:05 - run the entire graph system without you
Most people are still building one agent and calling it done.
Andrew Ng is already teaching what comes next:
Prompt → Agents → Loops → Graphs → Self-Improving Systems
Single agents are the old workflow.
Systems that improve and run without you are the next one.
This 2-hour course is worth more than most $500 agent engineering courses.
Bookmark it and watch before everyone starts catching up.
Then read how to run 1,000 agents from one prompt below ↓