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Who is a full stack AI engineer? These are the 8 skills for AI engineering you need to learn:


1. Prompt Engineering Learn how to craft structured prompts that robustly generate deterministic output.

2. RAG Systems RAG = Retrieval Augmented Generation This is where you give LLMs access to knowledge via databases (vector storage).

3. Fine Tuning Learn skills like LoRA to enhance a foundational LLM with domain-specific data.

4. AI Agents AI agents automate business processes and workflows with: - tools - multi-nodes - memory and state management

5. LLM Deployment Learn how to package your models into production-ready APIs.

6. LLM Optimization Learn how to improve results and lower inference costs by optimizing your AI applications.

7. LLM Observability Learn tracing, logging, and monitoring to ensure your AI application works as the underlying LLMs change.

8. Context Engineering Learn how to orchestrate content and enhance contextual inputs to your LLM models for domain-specific purposes.

Want to learn how to do this for a data science project? On October 1st, How I built an AI Customer Segmentation Agent with Python (Free Workshop): - Scikit Learn - K-Means - LangChain - LangGraph - OpenAI 👉Register here (500 seats): <a target="_blank" href="https://learn.business-science.io/ai-register" color="blue">learn.business-science.io/ai-register</a>


That's a wrap! Over the next 24 days, I'm sharing the 24 concepts that helped me become a data scientist. If you enjoyed this thread: 1. Follow me @mdancho84 for more of these 2. RT the tweet below to share this thread with your audience <a target="_blank" href="https://twitter.com/815555071517872128/status/1969787536475136450" color="blue">x.com/81555507151787…</a>