Building a Practical AI System Design for Solo Operators

You’ve tried cobbling together prompts in ChatGPT, but the output feels brittle and you waste time re‑explaining context every run. After reading this, you’ll be able to sketch a repeatable AI system — define inputs, choose a model, add memory, and hook it to a simple workflow — then decide whether to build it yourself or pull a pre‑made blueprint. You’ll learn a practical approach to ai system design that turns scattered prompts into a reliable pipeline.

Why do prompt chains break when you add more steps?

Most people start with a single prompt, get a decent answer, then chain another prompt on top. The first mistake is assuming the model remembers everything you said before. It doesn’t; each call sees only the text you send in that request. When you add a second step you have to re‑inject the prior answer, and any nuance gets lost.

Second, token limits creep up fast. A 500‑token prompt plus a 400‑token answer already eats 900 tokens. Add a third step and you’re near the 4k ceiling of many cheap models. The model starts truncating or returning garbled JSON.

Third, error handling is missing. If step one fails, step two still runs on garbage. You end up with a cascade of bad data and no way to know where it broke.

One‑sentence paragraph: Fixing this requires explicit memory and validation between each call.

Core components you actually need

Think of an AI system as four blocks: input collector, model orchestrator, memory store, and action dispatcher. The input collector can be a web form, a CSV upload, or an inbound email. The orchestrator decides which model to call, builds the prompt, and sends the request. The memory store holds the conversation state or any intermediate results you need later. The dispatcher takes the model’s output and turns it into an email, a CRM entry, or a Slack message.

You don’t need a fancy vector database for most solo ops. A simple Airtable base or a Google Sheet works fine for storing short‑term context. For longer term you might add a lightweight SQLite file, but start small.

The orchestrator is where most people get stuck. They try to do everything in one giant prompt. Instead, break the goal into discrete steps and let the orchestrator loop over them.

What most guides get wrong about memory and context

Many tutorials tell you to “just feed the whole chat history into each prompt.” That works for a demo but blows up in production. The history grows, token cost rises, and the model starts ignoring older bits because of attention decay.

What you really need is a summary layer. After each step, ask the model to compress the important facts into a short bullet list. Store that list, not the raw transcript. When the next step runs, prepend the summary to the fresh prompt.

I’ve seen guides suggest using a separate “memory model” that costs more than your main LLM. That’s overkill for a solo operator. A simple rule‑based extractor — like asking the model to output JSON with fields {next_action, key_fact} — does the job.

Concrete gripe: I got frustrated when a popular no‑code AI agent platform forced me to upgrade to a $79/mo plan just to enable custom JSON outputs, a feature that should be free for testing.

Concrete example: Building a lead‑gen outreach bot with Make, OpenAI, and Airtable

Let’s walk through a real scenario: you want to scrape LinkedIn titles, generate a personalized intro email, and log the result.

1. Input collector – Use Make’s HTTP module to call a public LinkedIn scraper API (or a simple Google Sheet where you paste URLs).
2. Model orchestrator – In Make, add an OpenAI module. Prompt: “You are a friendly sales assistant. Given the prospect’s name “{{name}}” and their title “{{title}}”, write a 90‑word cold email that mentions a recent post they shared. Keep the tone casual but professional.”
3. Memory store – Save the generated email and the original URL to an Airtable base. Use Make’s Airtable module to create a new record.
4. Action dispatcher – Optionally add a second Make step that sends the email via Gmail or SMTP.

Here’s the exact prompt I use (copy‑paste into the OpenAI module):

You are a friendly sales assistant. Given the prospect’s name "{{name}}" and their title "{{title}}", write a 90‑word cold email that mentions a recent post they shared. Keep the tone casual but professional.

Cost check: Each call uses about 350 tokens. At OpenAI’s $0.0005 per 1k tokens (GPT‑4o mini), that’s $0.000175 per email. Even at 200 emails a day you’re under $0.04.

Concrete love: I love how Make’s scenario history lets you replay each step with exact inputs and outputs, which turned debugging from guesswork into a quick click.

How to debug when this breaks: logs, fallbacks, and cost control

First, turn on Make’s execution logs. They show every module’s input and output, so you can see if the scraper returned empty data or if the OpenAI call threw a rate‑limit error.

Second, add a fallback. If the OpenAI module returns an error, route the data to a “manual review” Airtable view where you can write the email yourself. This prevents the whole scenario from stopping.

Third, watch token usage. Make lets you add a custom HTTP header to the OpenAI call that logs usage; you can also set a monthly cap in your OpenAI account to avoid surprise bills.

Mild aside: (which, yes, is annoying) the free tier of Make limits you to 1,000 operations a month, which feels tight when you’re testing multi‑step flows.

Pricing opinion: what’s fair for the pieces you need

OpenAI’s API is the cheapest part. At $0.0005 per 1k tokens for GPT‑4o mini, a modest lead‑gen flow costs pennies.

Make’s paid plans start at $9/mo for 10,000 operations. If you’re only running a few hundred scenarios a month, the free tier is enough for solo work.

Airtable’s free tier gives you 1,200 records and 2GB of attachment space — plenty for a starter bot.

I think paying $49/mo for a “no‑code AI agent” builder that bundles these pieces is overpriced when you can assemble the same thing for under $15/mo.

Final thoughts: build or blueprint?

If you enjoy tinkering, follow the steps above, swap in your own data sources, and you’ll have a working AI system in an afternoon.

Adjacent reading: deeper coverage of AI agent platforms.

If you’d rather skip the build and deploy a working version in an afternoon, we’ve packaged this workflow as a blueprint at deepusecase.com/vault.