Prompt Darwinism: Evolutionary Engineering in the Age of LLMs
summary
Argues that hand‑tuned prompts are brittle and demonstrates evolution of prompts using Genetic Pareto Optimization over generations with a NodeJS front end and Flask/Python backend tied to DSPI; applies mutation/selection to system prompts for extraction, text‑to‑SQL, intent classification and red‑teaming a Marvel guardrail, grading candidates with regex heuristics or an LM judge, running on ChatGPT‑4‑mini for cost; notes some goals converge in 1–3 generations while others require many runs and careful validation.
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chapters (23)
- 0:00🚀 Build Stuff Conference – Get Your Tickets!
- 0:28Introduction: Prompt Engineering, Magic, and the Need for Science
- 2:20Serg’s Journey From Past Build Stuff Talks to Today’s AI Reality
- 4:30The Explosion of LLMs Since ChatGPT
- 6:10What Large Language Models Really Are
- 8:00Why AI Agents Add Power—but Also Fragility
- 9:50The “Prompt Sandwich” and How Agentic Context Is Structured
- 12:20Why Prompt-Based Systems Become Messy and Brittle
- 14:00From Prompt Engineering to Context Engineering
- 15:30Why Prompts Should Evolve Instead of Being Hand-Tuned Forever
- 17:00Genetic Algorithms Explained With Practical Intuition
- 19:30How Evolutionary Search Applies to Prompt Design
- 21:00What Counts as a “Gene” in a Prompt Optimization Process
- 23:00Introducing Genetic Pareto Optimization for Prompts
- 25:10Demo Setup: Prompt Evolution Interface and Workflow
- 27:20Real-World Use Cases: Extraction, Text-to-SQL, Classification, and Guardrails
- 30:00Live Demo: Red Teaming a Marvel Guardrail Prompt
- 33:10Prompt Fitness, Metrics, and What Makes a Good Evaluation
- 36:00Challenges in Building Useful Prompt Test Sets
- 38:10Debugging Optimized Prompts and Interpreting Results
- 40:00Common Context Engineering Problems in Production Systems
- 42:00Data Quality, Privacy, Tooling, and Architecture Considerations
- 43:30Final Takeaways: From Prompt Wizards to Prompt Scientists
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