You can't do serious LLM work without connecting the two.
Prompt Engineering is hypothesis.
Evals are evidence.
- You guess what will work through prompt engineering.
- You prove what works through evals.
Without evals:- Prompt engineering becomes trial-and-error without feedback.
- You don't know if your changes helped or hurt performance.
Recommended Course
If you want to deepen your understanding of AI Evals, I highly recommend Hamel Husain's AI Evals For Engineers & PMs course. This hands-on 4-week cohort course covers practical approaches for improving AI applications, evaluation methods, and error analysis.
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🔒 Study Real Jailbreak Cases — Prompt Injection & Guardrails
- Study how jailbreaks work in theory: how models interpret roleplay, context switching, or indirect language.
- Read research papers & red-teaming case studies from:
- OpenAI
- Anthropic
- Stanford CRFM
- Alignment Research Center
- Look for: “Jailbreak ChatGPT” studies, “Prompt injection attacks”, “Red teaming LLMs”
- Explore how to prevent jailbreaks in assistants and RAG systems.
- Learn about prompt injection attacks in tools - LangChain, ChatGPT plugins, etc..
🧠 Try:
“Explain how prompt injections exploit model context windows and examples of mitigation strategies.”