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Day 2: Prompt Engineering & Context Structure

Knowing which model to use is only half the equation... you also need to know how to communicate with them effectively

Forget the clever tricks, the game changed, clarity beats cleverness now, and the people getting results are writing prompts that read like good briefs, not like magic spells

Format by model

Claude was trained with XML tags so it responds exceptionally well to structure like this:

<context>

background information here

</context>

<task>

specific instruction here

</task>

<format>

how to structure the output

</format>

GPT and Gemini work well with JSON when you need structured data back

The format isn’t magic, it’s about giving the model clear signals about what you want, XML tags function like section headers in a document, they reduce ambiguity and the model rewards clarity with better outputs

Chain-of-thought for hard problems

When you need the model to work through something complex, adding “let’s think through this step by step” before asking for an answer significantly improves results

This isn’t placebo, reasoning tasks show measurable improvement when you prompt the model to externalize its thinking process

Use it for math, logic, multi-step analysis, and debugging... skip it for simple questions where the extra thinking adds nothing

The system prompt formula

Effective system prompts contain four elements:

Role — who the AI should be, like “you are a senior financial analyst specializing in tech valuations”

Behavior — how it should interact, like “ask clarifying questions before making assumptions and acknowledge when you’re uncertain”

Constraints — what it should avoid, like “do not give specific investment advice”

Output structure — how to format responses, like “lead with a 2-sentence summary then provide supporting analysis”

A good system prompt converts a general-purpose AI into a specialized assistant for your specific workflow, and once you’ve built one that works, you can reuse it hundreds of times

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Now zoom out: context structure

Prompt engineering was the 2024-2025 skill, context engineering is the 2025-2026 skill

The shift recognizes that individual prompts matter less than the information environment you create around your AI interactions

Shopify CEO Tobi Lutke defined it as “the art of providing all the context for the task to be plausibly solvable by the LLM”

The four strategies:

Write: save context outside the active window using scratchpads and reference files the AI can access

Select: choose what enters context through RAG and dynamic retrieval rather than dumping everything in

Compress: summarize verbose information before including it

Isolate: use separate conversation threads or sub-agents for different contexts that shouldn’t mix

Today’s assignment for you:

build your first system prompt using the four-element formula for a task you do repeatedly, test it across Claude and Gemini, then set up a Claude Project... upload relevant documents, write custom instructions, and run your first conversation with persistent context, one focused project per task beats one massive project with everything

Stay curious, talk to you tomorrow.

CoolDeep AI
Helping you stay ahead with AI & productivity