Context Engineering Explained (5 Practical Tips)
🤝 Your team not maximizing Claude? I run 1:1 and team AI workshops for companies doing $10M+ per year: https://aibuilder.academy/yt/zKHSpwayPBU The best way to get good outputs from an LLM is by giving it the right inputs. This is the goal of context engineering. In this video, I explain what this is and share five practical tips for doing it. 📰 Read More: https://shawhin.medium.com/context-engineering-explained-a5dc3d4d2e5a?sk=3689d74dfc1347b213e7202fbaf92099 References [1] https://x.com/karpathy/status/1937902205765607626?s=46&t=Fn7nSLn1fPNvDZO1IMXTQg [2] https://github.com/jujumilk3/leaked-system-prompts [3] https://cookbook.openai.com/examples/gpt4-1_prompting_guide [4] https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview [5] https://research.trychroma.com/context-rot [6] https://youtu.be/982V2ituTdc [7] https://youtu.be/T9aRN5JkmL8 [8] https://youtu.be/DL82mGde6wo Intro - 0:00 What is Context Engineering? - 1:05 Prompt Engineering vs Context Engineering - 3:41 Tip 1: Writing Clear Instructions - 5:07 Tip 2: Use Structured Text - 8:55 Tip 3: Keep It Tidy - 11:05 Tip 4: Run Experiments - 12:52 Tip 5: Meta-prompting - 14:55 AI Cohorts - 15:50
