Real Workflows: Draft and Summarize

What You'll Need

⏱ Time: 15 minutes  |  📋 Tasks:

• Open your preferred chatbot: chatgpt.com, claude.ai, or gemini.google.com
• Have a draft email you need to send (for Workflow 1) and a long article or report you want to digest (for Workflow 2)
• Keep this lesson open in a second tab to follow along

Building Knowledge Isn't Having a Workflow

You've learned the pieces — FAINT (Frame, Aim, Include, Nix, Tone), tone control, document work, model selection, trust calibration. But knowing the pieces separately isn't the same as doing the work. The most common reason people stop using AI after trying it isn't that the tools are bad. It's that they never developed a repeatable workflow — a consistent process they could run without reinventing the approach every time.

This lesson gives you two workflows that cover 80% of what people use LLMs for: generating content and digesting information. Follow along in your browser. These aren't theoretical demonstrations — they're prompts you can paste and adapt starting today.

Workflow 1 — Draft an Email from Bullet Points

This is the workflow you'll use most often. Instead of staring at a blank screen, you dump your raw thoughts and let the model structure them.

Step 1: Open your chatbot and paste context

Paste something like this, filled in with your actual situation:

I need to write a follow-up email to a client who hasn't responded in 2 weeks. Here are the key points I want to include:
- We met on June 15 to discuss the proposal
- I sent the formal proposal on June 18
- I'm checking in, not pressuring
- I'm available to answer questions or adjust scope
- I'd like to suggest a specific time to talk next week

Notice what's happening: you're using the FAINT framework from Lesson 1 without thinking about it. Context (who you are, who the client is). Aim (a follow-up email). Include (the 5 bullet points). You haven't specified tone or constraints yet — that comes next.

Step 2: Add tone and structure constraints

Add this to the same chat:

Keep it to 4 sentences. Sound friendly but not pushy. Use contractions. End with a specific question that makes it easy for them to reply.

This is the Nix and Tone from FAINT — you're telling the model what to avoid and how to sound. The draft should now be shorter, warmer, and structured the way you want.

Step 3: Read and adjust

Read the draft. Does it sound like you? If not, make one small fix — replace a phrase, shorten a sentence, add a specific detail only you would know. Type something like:

Change "I wanted to check in" to "Just checking in." And add a sentence saying I'm flexible on timing.

Step 4: Ask it to cite sources

If your email references facts, data, or anything the reader might want to verify, add this:

You mentioned [specific claim]. Where does that information come from? Cite your source.

The model can't browse the web in a standard chat (unless you enable web search), but asking for sources does two things: it forces the model to ground its claims in something specific rather than generating plausible-sounding filler, and it flags areas where the model is speculating — if it can't produce a real source, you know to verify the claim yourself before sending.

Step 5: Iterate — one more constraint

Once you're close, try one final constraint:

Now make it shorter by 30%.

Watch the model cut different things than you would. Sometimes it keeps a phrase you'd remove and removes one you'd keep. This is the most instructive part — it shows you the difference between what the model considers essential and what you consider essential. You'll wind up with an email that sounds like you, structured how you want, in about 2 minutes instead of 15.

Workflow 2 — Summarize a Long Article

The second most common task: you have a long piece of content and need the key points fast.

Step 1: Get the text into the model

If the article is a clean digital file (no scanned pages, no columns), copy-paste the text or upload the PDF. If it's a web page, paste the URL if your chatbot supports it, otherwise copy the text directly.

Step 2: Ask for a structured summary

Paste with instructions:

Summarize this in 3 bullet points at a 10th-grade reading level. Then give me a one-paragraph summary for someone who hasn't read the original. Focus on concrete facts and conclusions, not general topic descriptions.

The two-level output is intentional: the bullet points give you the core facts, the paragraph gives you an explanation you could forward to someone else.

Step 3: Test the summary's accuracy

Ask a question you can verify from your own reading:

What's the single most important takeaway from this article? Use the author's own framing, not a general interpretation.

This forces the model to ground its answer in the specific document rather than generating a generic summary. If the answer matches what you remember, confidence goes up. If it doesn't, you know the model synthesized rather than extracted — and you need to check more carefully.

Step 4: Ask for what's missing

Ask for the other side:

What perspective does this article leave out or underrepresent? What questions does it raise but not answer?

This is the Challenge and Opposing from the SCOPE framework in Lesson 7 — it turns a one-sided summary into a more balanced understanding. Good for catching bias in the source material itself.

Handling documents longer than 10 pages

There's a real problem with long documents that most people don't anticipate: context confusion. As a conversation gets longer, the model's attention to earlier parts degrades. It starts mixing up sections — attributing a claim from chapter 3 to chapter 7, or forgetting a detail you discussed 20 messages ago. This isn't a bug; it's a fundamental limit of how these models work. They process input in a single pass, and the middle of a long conversation is where accuracy drops first.

This leads to context pollution: early messages about one topic contaminate later responses about a different topic. If you ask about a contract's payment terms early in a chat, then later ask about its termination clause, the model may blend the two — especially if both sections use similar language. The fix is to start fresh for each distinct question rather than piling everything into one conversation.

Here's how to manage it:

• Section-by-section processing: don't upload the whole document at once. Paste chapter by chapter or section by section and ask for a summary of each.
• Summarize the summaries: after processing each section individually, open a new chat and paste all the section summaries together. Ask for a final synthesis. This keeps each individual pass within the model's reliable range.
• One conversation per topic: if you're researching a document with multiple distinct topics (pricing, legal, timeline), use a separate chat for each. This prevents claims about one topic from bleeding into another.
• Start fresh when confusion appears: if the model starts mixing things up — attributing a fact to the wrong section, or repeating earlier points — that's your cue to open a new conversation. Don't try to correct it mid-stream; the confusion is from accumulated context, not from misunderstanding your latest message.

Your Takeaway — Two Reusable Workflow Templates

Save these two prompts somewhere you can copy-paste — notes app, bookmark, pinned message. They cover the two most common things you'll do with an LLM. With each use, tweak them to match how you actually work.

--- EMAIL DRAFT ---
I need a [type] email to [recipient].
Key points:
- [point 1]
- [point 2]
- [point 3]
Tone: [friendly / formal / urgent]
Length: [X sentences]
End with: [a specific question, a call to action, an offer]

--- SUMMARIZE ---
Summarize this in [X] bullet points.
Then give me a one-paragraph summary.
Reading level: [grade level].
Focus on: [concrete facts / conclusions / data points / arguments].

Try It

This week, run both workflows with real content from your life — an email you actually need to send, an article you actually want summarized. Don't use example content. Use something that matters to you, because that's the only way you'll feel the difference between a generic output and a useful one.

Save the best outputs. Next time you need to do the same kind of task, show the model your saved example first — "Write something like this, but about X." Examples are the most powerful prompt. After a few uses, tweak the templates above to match how you actually work: add your preferred phrases, remove options you never use, adjust the length defaults. The goal isn't to follow the template forever — it's to internalize the structure so you stop needing the template at all.