Six Ai Engineer skills, six projects. 1️⃣ RAG → hybrid search over internal docs with reranking and citations 2️⃣ Agents → orchestration system with tools, memory, and human in the loop 3️⃣ LLMOps → semantic caching layer that cuts your API bill in half 4️⃣ Evals → generator that builds test sets from real production logs 5️⃣ Fine tuning → LoRA pipeline on a domain dataset, base model to benchmark 6️⃣ Guardrails → text to SQL that blocks destructive queries before they run Comment "SKILL" and I will send you the full breakdown for each one + 9 more

Summary

The video outlines six essential AI engineering skills and corresponding projects to practice each skill effectively.

Key Points

1. RAG enables hybrid search over internal documents with citations.
2. Agents involve an orchestration system with memory and human input.
3. LLMOps reduces API costs with a semantic caching layer.
4. Evals generate test sets using real production logs.
5. Fine tuning uses a LoRA pipeline for domain-specific datasets.
6. Guardrails prevent destructive SQL queries from executing.

Tags

Repurpose Ideas

• LinkedIn post: Overview of six AI engineer skills.
• Tweet: Key projects for mastering AI engineering.
• Checklist: Steps to implement each AI project.