Prompt Engineering is Dead
summary
Claims a replaceable workflow: build an automated agent that crawls prompting guides, uses an LM-as-judge evaluator, and iterates prompts against a 20-question dataset with three expected facts per question (60 facts total). The RAG pipeline uses a Chroma vector DB plus OpenAI calls and the agent runs via Crew AI. Initial evaluator score is 0.4 and after two automated iterations the prompt score rises to 0.9, but the demo flags overfitting and recommends train/test splits before trusting generalization.
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chapters (7)
- 0:00The case against prompting
- 1:09RAG pipeline improvement
- 2:20Building the RAG pipeline
- 4:06Choosing an evaluator
- 6:12Setting up the evaluator
- 9:14The auto-improving agent
- 11:57Final thoughts and links
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