Workflow Optimization

Once you know the basics of automation, the next step is Optimization. This is about reducing "Latent Rubbish" (the steps that don't add value) and focusing on the core output.

1. Batching vs. Real-time

Should you process data as it comes in, or all at once?

• Real-time: Best for notifications and customer support (Slack, Email).
• Batching: Best for reporting, SEO updates, and newsletter generation (Google Sheets).

2. The "Pre-Processing" Step

Sometimes, ChatGPT fails because the input is too messy.

• Optimization: Use a simple script (or a simpler AI model like GPT-3.5) to clean the formatting, remove HTML tags, or fix spelling before the main GPT-4 model processes it. This saves tokens and increases quality.

graph LR
    Raw[Raw Messy Data] --> Clean[Pre-Process Cleanup]
    Clean --> GPT4[High-Reasoning Model]
    GPT4 --> Quality[High Quality Output]

3. Human-In-The-Loop (HITL)

Never automate 100% of a creative or high-stakes task.

• Optimized Workflow:
  • Step 1: AI generates 5 drafts.
  • Step 2: Human selects the best one.
  • Step 3: AI polishes and formats the selection.
  • Step 4: Human hits 'Send'.

4. Token Efficiency

Save money and time by asking for "Skeleton" responses.

• "Summarize this article in 5 bullet points. Do not include an intro or outro. Just the bullets."

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Hands-on: Streamline Your Day

1. List the 3 tools you use the most (e.g., Email, Slack, Trello).
2. Strategy: What is ONE piece of data that moves between them manually?
3. Task: Write a prompt that would handle the "Translation" or "Formatting" of that data automatically.

Key Takeaways

• Pre-processing saves tokens and improves reasoning.
• Human-in-the-loop prevents high-cost errors.
• Skeleton prompts are faster and cheaper.