🧑🍳 Kitchen Prep
The difference between a useful AI response and a generic one is almost always context. Here's what to give it and why.
Why context matters
AI doesn't know who you are, what you're working on, or what a good answer looks like for your situation. Without context, it defaults to the most average possible response — technically correct, practically useless.
Think of it like briefing a new hire. The more relevant background you give, the better the work you get back.
The 5 context ingredients
Goal
What are you actually trying to accomplish? Not just "write an email" but "write an email that gets a meeting with a cold prospect who's seen 50 pitches."
Audience
Who is this for? Their background, what they know, what they care about, and what would make them tune out.
Format
What does the output need to look like? Bullet points, paragraphs, a table, a specific word count, a specific tone?
Constraints
What are the limits? What can't you do, what must you include, what would be a deal-breaker?
Examples
What does good look like? Even a rough example — "something like this but shorter" — dramatically improves results.
Before vs. after
The role frame shortcut
One of the fastest ways to add context is a role frame — telling the AI who it should be. This calibrates tone, vocabulary, and the type of advice it gives.
Instead of: "Give me feedback on my code"
Try: "You are a senior engineer doing a code review. Your goal is to catch bugs and flag anything that won't scale. Be direct — don't soften criticism."
Browse ready-made role frames in The Pantry or build your own in the Spice Rack.
Follow-up prompts that fix almost anything
A single prompt is rarely the end of the conversation. These follow-ups work after any response to reshape, sharpen, or redirect:
Think of your session as a conversation, not a single question. Iterative follow-ups consistently outperform trying to write one perfect prompt upfront.
Quick checklist
Before you hit send, ask yourself:
- Does it know what I'm trying to accomplish?
- Does it know who this is for?
- Does it know what format I need?
- Have I told it what to avoid?
- Would a smart person reading only this prompt know what a good answer looks like?
If yes to all five — you're ready to cook.
Prompt engineering is evolving
From single mega-prompts to sequential chains — and why the shift produces dramatically better results
average improvement in output quality when switching from single complex prompts to 5-step chains — measured across writing, data parsing, and code generation workflows over 30 days.
The problem with mega-prompts
When you pack everything into one giant prompt — context, instructions, format requirements, edge cases, examples — you're asking a single cook to prep, cook, plate, and run the whole restaurant at once. The model spreads thin.
Single mega-prompts break down in predictable ways:
- Hallucinations multiply. The more you ask at once, the more the model fills gaps with invented details to satisfy competing demands.
- Focus dilutes. Competing instructions create ambiguity — the model optimizes for some requirements at the expense of others, and you can't predict which.
- Context drift. In a long prompt, early constraints get de-prioritized as the model generates later parts of the response. The instruction you put first is often the one it "forgets."
- No checkpoints. You can't catch errors mid-process. You get the whole output or start over — there's no place to course-correct.
The solution: the 5-step chain
Prompt chaining breaks one complex workflow into ~5 sequential micro-prompts where Output A becomes Input B. Each prompt does exactly one thing, and does it well.
Establish scope, key angles, or raw material. Keep this prompt narrow.
Take the output of step 1 and build a skeleton — outline, schema, or plan.
Work through the structure one section at a time. Focus the model on a single chunk.
Pass the draft back in. Ask for a specific improvement — tighten, cut, or deepen. One lens only.
Apply final structure, tone, or output requirements to the polished draft.
Each step is focused, verifiable, and correctable before it feeds the next. You can swap, repeat, or branch any step without restarting from scratch.
Before vs. after: writing an article
Result: generic structure, shallow comparisons, the code doesn't match the tools in the table, the CTA is boilerplate. Total rewrite needed.
Step 1 — Research
"List the 5 most-used vector databases for production ML apps in 2025. For each: one-line summary, primary use case, and one concrete limitation."Step 2 — Outline
"Using this comparison [paste step 1 output]: write a 7-section outline for a 1,200-word technical blog post for mid-level developers. Each section gets a heading and 2-sentence description of what it covers."Step 3 — Draft section by section
"Write section 3 of this outline in full [paste section 3 description]. Tone: direct, no padding. Include one Python snippet showing a real insert operation."Result: each section is coherent, the code matches what was discussed, the comparison table is accurate. Minor edits only.
Methodology and benchmarks via @godofprompt — 30-day structured testing across writing, parsing, and code generation workflows.
📚 Further reading
Want to go deeper? These are the best technical pieces on context engineering, memory, cost, personas, and output reliability.
Effective Context Engineering for AI Agents
A developer's guide to structuring context windows, managing token budgets, and feeding agents the right information at the right time.
Read article →How AI Agents Remember Things: Vector Stores in LLM Memory
Explains how agents persist information across sessions using vector databases — the technical foundation behind AI that actually remembers.
Read article →How to Compress Your Prompts and Reduce LLM Costs
Practical techniques for trimming prompt tokens without losing quality — useful once you're running prompts at any scale.
Read article →Functional Personas: A Lean, Practical AI Workflow
How to build reusable AI personas that encode role, tone, and constraints — making your prompts consistent and portable across tools.
Read article →How to Keep LLM Outputs Predictable Using Pydantic Validation
Schema-based validation to enforce structured outputs from language models — essential reading if you're piping AI output into code.
Read article →How Do AI Agents Work?
A clear walkthrough of the perceive-reason-act loop that powers every AI agent, with a real-world email automation example using the OpenAI API.
Read article →The Best AI Models for Coding: Accuracy, Integration & Developer Fit
JetBrains' breakdown of which AI models perform best for real development tasks — comparing accuracy, IDE integration depth, and fit for different developer workflows.
Read article →Claude Code Ultimate Guide
The best community-built resource for Claude Code — 181 templates, 271-question quiz, agentic workflow patterns, hooks, plugins, and a security threat database.
Read article →Effective Context Engineering
Anthropic's own engineering guide to context — how to structure what you give a model, when to compress, and why context quality is the highest-leverage variable in agent performance.
Read article →Agent Memory & Context Lifecycle
How agents manage what they remember across a session — in-context storage, external memory, summarisation strategies, and when each approach breaks down.
Read article →