Why prompt libraries are a waste of your time — and what to build instead.
Last month, a client called me frustrated. He'd spent $200 on a "premium prompt library" — 500+ prompts organized by category, beautifully formatted, supposedly the key to unlocking AI's full potential.
"I tried twenty of them," he told me. "They all give me the same generic garbage."
I laughed, because I'd been in the exact same place a year ago. I had folders full of saved prompts. "Act as a world-class marketing strategist who specializes in..." You know the type. And every single one gave me output that sounded impressive but could have been written for literally anyone on Earth.
The fix wasn't a better prompt. It was a single document I now use across Rocket Media, Digital Ignitor, and even for managing my household of 11. I call it a context file, and once I started building them, AI went from a fancy autocomplete to something closer to a thinking partner who actually knows my situation.
Here's what I built, how I use it in practice, where it broke, and exactly how you can build your own in the next 30 minutes.The Problem Isn't Your Prompts — It's Your AI's Amnesia
Here's what clicked for me after months of trial and error: every time you open a new AI conversation, you're talking to someone with total amnesia. Brilliant, well-read amnesia — but amnesia nonetheless.
When I type "write me a follow-up email for a prospect who went quiet," the AI has to guess everything. What industry? What's my voice? What did we already discuss? What's my closing style? Am I selling a $500 service or a $50,000 engagement?
Without that context, it does what any reasonable person would do — it writes something that works for nobody in particular.
I discovered this the hard way at Rocket Media. I was using AI to draft client communications, and the tone was all wrong. Too formal for our HVAC contractors. Too casual for the solar companies. And the recommendations were generic because the AI didn't know our clients' actual situations.
So I started building context files. Not prompt templates. Not instruction sets. Living documents that tell AI who I am, what I'm working on, and what actually matters — before I ever ask it a question.
What a Context File Actually Looks Like (With My Real Template)
A context file is just a document you paste at the start of any meaningful AI conversation. Think of it as a briefing packet — the same thing you'd hand a new employee on their first day, so they don't spend three weeks asking obvious questions.
Here's the exact structure I use. Steal this:
Section 1: Who I Am and What I Do Keep this to 2-3 sentences. Not a resume — just the relevant details.
My version: "I run Rocket Media, a marketing agency serving home services companies (HVAC, plumbing, electrical, solar) since 2003. I also run Digital Ignitor, an AI strategy consulting firm. I have a household of 11 — my wife Lindsay and our 9 kids."
Section 2: What I'm Working On Right Now 3-5 current priorities. This section changes the most often.
My version:
Building a conversation-to-strategy system that extracts client positioning from dialogue instead of questionnaires
Testing AI phone answering for Rocket Media (6 months in, mixed results)
Developing a voice-first lead qualification workflow
Writing weekly content for bensaibrain.com, documenting AI experiments
Section 3: How I Think and Communicate Your voice, your style, your non-negotiables about tone.
My version: "I'm direct and story-driven. I lead with personal experiences, not theory. I share failures as openly as successes. I hate corporate jargon — if it sounds like a consulting deck, rewrite it. I'd rather be helpful than impressive."
Section 4: My Constraints What you won't do matters as much as what you will.
My version: "I have limited deep-focus time because I'm splitting across multiple businesses and family. I prefer rapid prototypes I can test in hours, not polished plans I'll never execute. I won't sacrifice family time for productivity. Solutions need to be sustainable, not just fast."
Section 5: What Success Looks Like Specific and measurable. Not "grow my business."
My version: "Generate three qualified consulting leads per week through content that demonstrates real expertise. Build AI systems that create capacity for family time, not more screen time. Productize at least two internal solutions into client-facing services this quarter."
Total length: about 300-500 words. That's it. Takes 20-30 minutes to write the first time. I keep mine in a simple text file I can copy and paste from.
How I Actually Use This (4 Real Workflows)
A context file sitting in a Google Doc doesn't help anyone. Here's how I actually deploy mine across different parts of my life, with the exact steps and prompts I use.
Workflow 1: Client Work That Doesn't Sound Generic
This is where context files first clicked for me. At Rocket Media, we serve very different types of companies — a plumbing company in Phoenix and a solar installer in Dallas need completely different messaging. AI doesn't know that unless you tell it.
Step 1: I paste my base Rocket Media context file. This includes our agency positioning, the industries we serve, and our communication style.
Step 2: I add the specific client context. Here's what that looks like:
"Client context for this session: ABC Plumbing is a family-owned plumbing company in [city], been in business 15 years. They're known for same-day service and their owner is the face of the brand — friendly, blue-collar, trustworthy. Their biggest competitor is running aggressive discount campaigns. ABC doesn't want to compete on price — they want to compete on trust and speed. Their customers are homeowners, mostly 35-55, dealing with urgent problems like burst pipes or water heater failures."
Step 3: Now I ask for what I actually need:
"Based on this context, draft three follow-up emails for homeowners who requested a quote but haven't responded in 5 days. Match ABC's voice — no corporate speak, no pushy sales language. These people have a plumbing problem, not a 'service opportunity.'"
Step 4: I review the first draft and refine with a follow-up:
"The tone is close but too polished. ABC's owner talks like a neighbor, not a marketer. Make it sound like someone who actually fixes pipes for a living wrote this."
The difference: Without context, I get "Dear Valued Customer, we wanted to follow up on your recent inquiry..." With context, I get something that sounds like it actually came from a plumber who cares about the person's flooded basement. That gap is everything in our business.
Workflow 2: Making Decisions Across Multiple Businesses
Here's where context gets really powerful — and where I think most people underuse AI entirely. I don't just use context files for creating content. I use them for thinking through decisions.
Last quarter, I was trying to figure out whether to productize an internal tool we'd built. It worked great for us, but would it work for clients? I walked AI through the decision like this:
Step 1: Paste my Digital Ignitor context file (positioning, current services, target clients, pricing model).
Step 2: Add the specific decision context:
"I built an internal tool that extracts brand positioning and voice from client conversations instead of written questionnaires. It's working well for Rocket Media — our intake completion improved significantly and the quality of what we capture is much better. I'm considering turning this into a standalone service through Digital Ignitor. Before you give me advice, I want you to ask me 7 hard questions about this decision that will expose my blind spots."
Step 3: Answer the questions honestly. This is the critical step most people skip. The AI asked me things like "What's your maintenance burden if you have 20 clients using this simultaneously?" and "How do you handle it when the AI extracts positioning that the client disagrees with?" Questions I hadn't fully thought through.
Step 4: After answering, ask for a structured recommendation:
"Now that you have my context and my answers, give me a recommendation. Should I productize this now, later, or not at all? What's the biggest risk I'm underestimating? What would you do with my exact constraints — limited time, multiple businesses, small team? Be blunt."
The key insight: AI with context doesn't just give better answers. It asks better questions. And the questions are often more valuable than the answers.
Workflow 3: Content That Sounds Like Me (Not Like AI)
This is the workflow I use most frequently, because I publish content weekly for Ben's AI Brain, and it has to sound like me — curious, story-driven, willing to share failures. Generic AI writing is the opposite of that.
Step 1: Paste my content creation context. Mine includes my writing voice, my audience, recent topics I've covered, and my content calendar structure.
Step 2: Instead of asking AI to write the post, I ask it to help me find the angle:
"I want to write about [topic]. Based on my voice and audience, give me 5 angles that would make someone who already reads AI content stop and think 'I haven't seen this take before.' No generic listicles. No 'top 10' anything. I want angles grounded in real experience, not theory."
Step 3: I pick an angle and ask for structure, not a draft:
"Take angle #3 and outline a full post. I want: a story-based opening that shows a real moment or problem, 4-5 main sections each built around something I've actually tested or experienced, and a closing that invites the reader in rather than lecturing them. Don't write the post — just give me the skeleton."
Step 4: Only after I like the outline do I start drafting, section by section:
"Write the opening section only. Remember — I open with stories and scenes, not announcements. Something happened, someone said something, I realized something. Then stop."
Step 5: I edit heavily. Always. Context gets AI 70% of the way there. The last 30% is me adding the details, the voice inflections, the specific memories that no AI can invent.
Why this matters: Content is where bad AI output is most visible. Your audience can smell generic in two sentences. Context files don't make AI write like you — they make AI write close enough that your edits are refinements, not rewrites.
Workflow 4: Family and Life Operations (Yes, Really)
I have a context file for my family. If that sounds weird, consider this: with 9 kids ranging across different ages, coordinating schedules, preferences, dietary needs, vehicle logistics, and gift planning is a genuine operations problem.
Step 1: My family context file includes basics — names, ages, current activities and interests, dietary considerations, vehicle assignments, and home maintenance priorities.
Step 2: For something like holiday planning, I add seasonal context:
"Christmas planning for 11 people. Budget is [X]. I need gift ideas for each person based on their current interests and what they already received for birthdays this year. For each suggestion, tell me why it fits this specific kid — not generic 'kids love this' recommendations."
Step 3: The output is dramatically better than searching "gift ideas for 12-year-old boy." Because AI knows THIS 12-year-old is into robotics, already has a LEGO Mindstorms set, and mentioned wanting to learn about drones during a family dinner.
Step 4: I use the same approach for meal planning, vacation logistics, and even scheduling:
"Based on our family context — 9 kids in different activities, two working parents, five vehicles — help me map out next week's transportation logistics. Flag any conflicts where two kids need to be at different locations at the same time with no available driver."
The deeper point: This isn't about being an "optimized" family. It's about capacity. Every minute I save untangling logistics is a minute I spend actually being present with my kids. AI handles the spreadsheet-brain work so I can focus on the human parts — the conversations, the moments, the being-there.
I Don't Have One Context File. I Have Seven.
Those four workflows hint at something that took me a while to figure out: one context file doesn't cut it when you operate across multiple domains.
The way I talk to clients at Rocket Media is different from how I approach AI consulting at Digital Ignitor. The problems I'm solving for pool chemistry have nothing to do with lead qualification workflows.
So I built separate context packs:
Rocket Media context — client industries, service model, typical pain points, competitive positioning, our voice (direct, a little sass, industry-specific).
Digital Ignitor context — strategic consulting positioning, the types of problems clients bring us, how we approach AI implementation, and case studies we reference.
Content creation context — my writing voice, who I'm writing for, topics I've covered, and content calendar structure.
Client project contexts — individual files for active engagements with their industry, goals, brand voice, and what we've already built.
Family operations context — schedules, preferences, vehicle fleet, home maintenance priorities.
Personal projects context — current builds in my lab, tools I'm testing, what's working, and what's failing.
When I start an AI session, I load the relevant pack. Working on a blog post? Content creation context. Drafting a proposal for a plumbing company? Rocket Media context plus their client file. Planning a family vacation? Family operations.
The difference is immediate. Instead of spending the first five exchanges teaching AI who I am, I'm getting useful output from the first response.
Where This Broke Down (And What I'm Still Figuring Out)
I'd love to tell you this system worked perfectly from day one. It didn't.
The staleness problem. My first context files were static — I wrote them once and pasted the same document for months. But my businesses change. Client situations evolve. What I was working on in October wasn't what mattered in January. I was feeding AI outdated context and getting recommendations based on problems I'd already solved. I now update context files at least monthly, and the active projects section is refreshed weekly.
The too-much-context problem. I went through a phase where my context files were 2,000+ words. I thought more detail meant better output. What actually happened was the AI would latch onto random details and weight them too heavily. My Rocket Media context mentioned one specific client success story, and for weeks, every piece of marketing advice referenced that same example. Shorter and more focused beats are comprehensive every time. 500 words max for base context.
The false confidence problem. This one's subtle and still trips me up. When you give AI good context, the output sounds much more authoritative. It references your specific situation, uses your terminology, and speaks to your actual challenges. That makes it dangerously easy to accept without questioning. I caught AI giving me a content strategy recommendation that sounded perfect—specific, actionable, and tailored to my actual audience. Except that the underlying logic was wrong. It was pattern-matching my context against generic marketing advice and dressing it up in my language. The context made the bad advice harder to spot, not easier.
That last one is worth sitting with. Context files make AI dramatically more useful, but they also make AI more convincing when it's wrong. You still need to think critically about every recommendation. Maybe more critically, because your guard drops when the output feels so personalized.
The Cross-Domain Insight That Surprised Me
Here's what fascinates me about running context systems across such different domains: patterns emerge that you'd never notice otherwise.
The context file I built for our client positioning system — where we extract brand voice and strategic positioning from conversations instead of questionnaires — shares architecture with the family preference database I built to track what 11 people actually want for birthdays and holidays.
Both are fundamentally solving the same problem: how do you capture what makes someone unique in a format that AI can actually use?
For clients, it's their competitive advantages, their tone, and their target audience's pain points. For my kids, it's their current interests, their sizes, and what they've mentioned wanting in passing. Different domains, same underlying challenge — building structured context from unstructured human information.
That cross-domain thinking wouldn't have developed if I had only used AI in one area of my life. When you build context systems for a marketing agency AND an AI consulting firm AND a household of 11, you start seeing the deeper patterns underneath.
Build Your First Context File Right Now (Step by Step)
If you're still winging it every time you open an AI tool, here's exactly how to start. No theory — just do this.Step 1: Set a 20-minute timer. Open a blank document — Google Doc, Notion, plain text file, whatever you'll actually use again.
Step 2: Answer these five questions. Write 2-3 sentences for each, not paragraphs. Fight the urge to be comprehensive.
What do you do, and who do you do it for?
What are you actively working on this month? (3-5 things max)
How do you communicate? What does your voice sound like? What do you hate in writing or conversation?
What are your real constraints? (Time, budget, team size, energy, non-negotiables)
What does success look like for you in the next 6 months? Be specific.
Step 3: Save it somewhere you can copy/paste from instantly. If it takes more than 10 seconds to find, you won't use it. I keep mine pinned in a notes app.
Step 4: Use it for real work today. Not a test run. Not "let me try this on something trivial." Open an AI tool, paste your context file, and add this line at the bottom:
"This is my base context. Before we start, summarize what you understand about my situation and flag anything that seems unclear or contradictory."
This forces the AI to internalize your context and gives you a chance to catch misunderstandings before they compound.
Step 5: After the session, spend 2 minutes noting what AI got wrong or missed. That tells you exactly what to add to your context file. This is how the file improves over time — through real use, not theoretical planning.
Step 6: After a week of using it, create a second context file for a different domain of your life or work. You'll feel the difference between "AI that knows this version of me" versus "AI that's still guessing."
The Deeper Principle I Keep Coming Back To
The reason context files work isn't complicated. AI is a pattern-matching engine. Better input patterns produce better output patterns. Generic input gets generic output. Specific, structured information about your actual situation gets output that applies to your actual life.
But here's what I find myself thinking about more and more: this isn't just an AI technique. It's a thinking discipline.
Building a context file forces you to articulate things you've never had to put into words. What does your voice actually sound like? What are you really working toward? What are you unwilling to compromise on? Most people have vague intuitions about these things, but have never written them down.
The act of building the context file is almost as valuable as using it. It makes you clearer about who you are and what you're trying to do. The AI just benefits from that clarity as a side effect.
I started building context files to get better AI output. What I got was a clearer understanding of my own businesses, my priorities, and what actually matters. The AI improvement was almost a bonus.
What I'm Building Next
I'm working on something I'm calling a "living context system" — where the context files update themselves based on what happens in my AI conversations. If I make a decision, the context reflects it. If a project finishes, it moves to a "completed" section. If a new priority emerges, it surfaces automatically.
I'm not sure it'll work yet. The prototype is rough, and I keep running into edge cases where automated updates introduce errors. But the idea of context that evolves without me manually rewriting documents every month — that's worth exploring.
I'll share what happens with the living context system as I build it. Including when it inevitably breaks.
If you're a business owner trying to figure out how AI actually fits into your operations — not the theory, but the real implementation — that's exactly what we help with at Digital Ignitor. We build these kinds of systems with you, tailored to your specific business. [Book a conversation] if you want to explore what that looks like.
Something here you disagree with? A context approach that's working better for you? I'd genuinely love to hear it.