Ep 848: Context Engineering: How to Get Expert-Level Outputs From AI Chatbots (Start Here Series Vol 7)

How did prompt engineering die so quickly? ☠️

And what the heck does context engineering even mean? 

One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results. 

But if the engine is constantly changing, then you also have to change how you drive and the roads you take. 

That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills. 

Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. Evolution from Prompt to Context Engineering
2. Why Prompt Engineering Is Now Obsolete
3. Defining Context Engineering in AI Chatbots
4. Six-Part Framework for Context Engineering
5. Four Layer System for Structuring AI Context
6. Building Reusable Context Vaults and Skills
7. Connecting Business Data to AI Models
8. Techniques to Achieve Expert-Level AI Outputs
9. Importance of Context Windows in Large Language Models
10. Context Engineering Best Practices and Scalability

Timestamps:

00:00 "Access AI Community & Tools"

03:08 "Mastering Context in AI"

07:23 "Smart Models Require Less Precision"

12:01 "Context Engineering Beats Prompt Engineering"

15:49 "AI Context: Six Key Blocks"

16:47 "Building Context for Better Results"

19:53 "AI: Training, Not Easy Button"

25:17 "Chain of Thought Prompting Decline"

29:11 "Show, Don't Tell Techniques"

32:13 "Context, Reuse, and Scalable Systems"

33:19 "AI Chatbots: Memory and Skills"

Keywords: 

context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don’t tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems,

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