AI automation

ChatGPT 5 Update: Create Smarter AI Workflows Fast

Charles Higgins

Charles Higgins

— 11 min read
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We recently watched a concise, practical walkthrough by Andrew George that demonstrates how to bring the new ChatGPT 5 models directly into your platform’s workflow editor. In this article, we’ll summarize what Andrew covered, expand on practical use cases, and provide step-by-step guidance and cost-conscious strategies so we can start building smarter, faster automations today.

Table of Contents

Why this update matters for our business

We’re building systems to save time, reduce repetitive tasks, and deliver personalized experiences to prospects and clients. Integrating an advanced language model directly into our workflow editor means we can automate everything from lead qualification and follow-ups to dynamic content creation and conversational bots — without flipping between tools or adding complicated glue code.

This update matters because it gives us flexibility: three model tiers tuned for different needs (deep reasoning, balanced performance, and speed/cost). That means we can choose the right level of intelligence for each automation and avoid overpaying for tasks that don’t require heavy reasoning.

Quick overview of the three ChatGPT 5 models

There are three distinct variants we can pick from when we add a ChatGPT action to our automations:

  • GPT-5 (full) — Best for deep thinking, complex reasoning, and tasks that require nuance, long-form drafting, or multi-step logic.
  • GPT-5 Mini — The middle ground. Cost-efficient but still very capable for complex prompts, structured outputs, and multi-part responses.
  • GPT-5 Nano — The fastest and cheapest. Ideal for conversational responses, short message generation, and any task where speed and low cost are more important than deep reasoning.

How the cost model works (tokens explained)

Understanding token pricing is the most important factor for keeping our automation costs predictable. Instead of paying per request, we pay per token. Here’s how to think about it:

  • Tokens are pieces of text. One token is roughly three quarters of one word. So a 100-word prompt is about 75 tokens. This is a handy rule of thumb for estimating costs.
  • Inputs and outputs are billed separately. Inputs are the text we send (prompts, system messages, variables), and outputs are what the model returns (responses, generated content, etc.).
  • Pricing tiers (per 1 million tokens) — Use these example numbers to plan: GPT-5 (full) ≈ $1.25 per 1M input tokens and $10 per 1M output tokens; Mini ≈ $0.25 per 1M input tokens and $2 per 1M output tokens; Nano ≈ $0.05 per 1M input tokens and $0.40 per 1M output tokens.

All costs above are presented per million tokens. That might sound abstract, so let’s translate it into practical terms:

  • A short prompt of 10–20 words uses only a few tokens, and costs will be negligible no matter which model we use.
  • A longer prompt that includes multiple fields, context from the CRM, and system instructions can push input tokens higher. If we pass long conversation histories into each call, input tokens add up quickly.
  • Similarly, a long output such as a 1,000-word article will use significantly more output tokens than a short confirmation message. For long generative tasks, the full GPT-5 model will cost more per output token than the Mini or Nano tiers.

Practical example from a test run

To see how the three models behave in practice, we ran a simple, identical prompt through every model: “I’m thinking about traveling to Paris this winter. Any tips?”

  • GPT-5 (full) returned an in-depth, structured, and context-aware response, including suggestions for neighborhoods to stay in, cultural tips, safety notes, and seasonal considerations. It read like a mini-guide and included nuanced reasoning about trade-offs.
  • GPT-5 Mini produced a compact, useful bullet list with practical suggestions — a middle-ground response that balances length and detail.
  • GPT-5 Nano generated a conversational reply with a few useful tips and follow-up questions, making it ideal for engaging someone in a chat and collecting more information.

That example highlights an important point: while the full model provides the richest content, the Mini and Nano options still return excellent, actionable responses — often more suitable for CRM messages, text threads, or conversational bots where brevity and speed matter.

Which model should we use for common automation scenarios?

Choosing the right model depends on the task, desired level of reasoning, latency tolerance, and our budget constraints. Here’s a practical guide:

  • Deep content generation (long-form emails, detailed proposals, complex summaries): choose GPT-5 (full).
  • Structured outputs & templates (summaries, bullet lists, multi-step instructions): Mini is often the best fit.
  • Conversational flows & quick replies (chatbots, SMS responses, short follow-ups): Nano is ideal due to speed and low cost.
  • Mixed workflows (qualification + escalation): use Nano or Mini for initial interactions; escalate to GPT-5 (full) only when the case requires complex reasoning or in-depth content.

Step-by-step: Adding ChatGPT 5 to a workflow

We can integrate any of the three models into our workflows using the platform’s automation action that supports ChatGPT. The steps below are general and will work inside most workflow editors that provide a ChatGPT or AI action.

  1. Add a ChatGPT action — In the workflow editor, add the ChatGPT action to the step where you want AI responses.
  2. Select the model — Choose between GPT-5 (full), Mini, or Nano depending on the task (refer to the guidance above).
  3. Assemble the prompt — Combine any dynamic fields from the CRM (lead name, inquiry, last message), a short system or instruction line, and the user-facing prompt.
  4. Set maximum tokens or character limits — To control cost and output length, set a maximum output length where possible.
  5. Tune temperature and style — If the editor exposes temperature or style options, set them to produce the voice you need (lower for predictable, higher for creative).
  6. Test and iterate — Run several tests with sample leads and real conversation histories to see how the model responds.
  7. Monitor costs — Track token usage and adjust model choices or prompt lengths if costs spike.

Practical prompt design tips to reduce costs and improve responses

Good prompts save us money and time. Here are practical ways we can make prompts more efficient and effective:

  • Be concise but specific — Include only the most relevant context. Long, verbose histories increase input token usage.
  • Use variables smartly — Pull in only the fields we actually need for the response (e.g., name, last product viewed), not the entire contact record.
  • Prefer Mini or Nano for repetitive interactions — For common templates or short replies, the lower-cost models will produce great results at a fraction of the price.
  • Keep a separate step for complex tasks — If a lead asks for a long report, use a workflow branch that triggers the full GPT-5 model for that single task rather than defaulting to it for every contact.
  • Use system instructions for consistent tone — A short system-level instruction like “Use a friendly, professional tone and under 100 words” reduces the need for repeated corrections in follow-up calls.
  • Limit output length — Use the model’s max token or character settings to prevent unexpectedly long outputs for simple requests.

Examples: Prompts for everyday automations

Below are ready-to-use prompt templates we can drop into our workflows. Replace variables with the platform fields for contact name, product, appointment date, etc.

  • Appointment confirmation (Nano)
    Prompt: “Confirm the appointment with {{contact_name}} for {{date}} at {{time}}. Keep it under 40 words and friendly.”
  • Lead qualification (Nano or Mini)
    Prompt: “Ask 3 short questions to qualify {{contact_name}} for {{service}}: budget range, timeline, key priority. Keep each question concise and friendly.”
  • Follow-up after demo (Mini)
    Prompt: “Draft a 4-paragraph follow-up email to {{contact_name}} after their demo of {{product}}. Recap their top concerns: {{concern1}}, {{concern2}}. Include a call-to-action to schedule a next step.”
  • Customer success summary (Full GPT-5)
    Prompt: “Summarize this customer conversation and provide 5 recommended next actions, prioritized by impact. Conversation: {{conversation_history}}.”
  • Short social post (Mini)
    Prompt: “Write a 30–40 word social post promoting our latest case study about {{topic}}. Use a professional, optimistic tone and include a clear call-to-action.”

Real-world use cases and how they save us time

Here are specific scenarios where integrating ChatGPT 5 makes a measurable difference:

Automated lead qualification

We can program a conversational flow that asks short, targeted questions to determine intent, timeline, and budget. With Nano handling the initial exchange, we save human hours and only send warm, qualified leads to our sales team. This reduces friction and speeds up response time — leads get answers immediately, and our team gets higher-quality prospects.

Dynamic follow-ups

Using Mini to draft follow-up messages tailored to each prospect’s answers allows us to keep messages personal without manual writing. The AI can reference a key objection, recap the demo, and propose a next step in a way that feels human.

Personalized nurturing sequences

We can generate targeted sequences for different buyer personas. Mini or Nano can produce short, personalized messages at scale, preserving the human touch while automating repetitive work.

Internal summaries

After client calls or long email threads, the full GPT-5 model can condense key points, decisions, and action items for internal handoffs. That saves time in meetings and reduces miscommunication.

Conversational bots

For chat widgets and text messaging where immediate, succinct replies are important, Nano is perfect. It answers common questions, collects details, and escalates to a human when conversations become complex.

Cost-control strategies

We always want to balance responsiveness with predictable costs. Here are strategies that keep costs down while preserving effectiveness:

  • Default to Nano or Mini for the majority of interactions. Use full GPT-5 only when necessary.
  • Trim prompt context — Pass essential fields only. Don’t send full conversation histories unless the task demands it.
  • Use shorter outputs — Limit generated text length where possible. Shorter responses are usually sufficient for most CRM and messaging tasks.
  • Batch heavy tasks — For long-form content generation, run jobs in batches during off-peak times and clearly define output expectations to minimize re-runs.
  • Monitor token usage — Track how many tokens each workflow consumes and identify expensive steps that can be optimized (e.g., replacing full model calls with Mini).
  • Use human-in-the-loop — For expensive outputs, have a single human review and approve AI drafts rather than generating fresh replies for every contact request.

Troubleshooting and best practices

We might run into common issues when introducing these models into workflows. Here are practical fixes and best practices:

  • Inconsistent tone or style: Add a short system instruction at the start of the prompt that sets voice, length, and formality.
  • Too-long outputs: Use max token/character constraints or explicitly ask the model to “keep it under X words/characters.”
  • Irrelevant answers: Reduce context that confuses the model and give a clearer, more focused prompt.
  • Unexpected costs: Audit the automation to find where long prompts or long outputs are generated and switch to Mini/Nano if applicable.
  • Latency issues: Use Nano for rapid conversational replies where speed matters; reserve bulkier models for asynchronous tasks.

Security and privacy considerations

When we feed contact information and conversation histories into an AI action, we must be careful about what data we pass. Best practices include:

  • Only pass the minimum required contact fields into prompts.
  • Avoid including sensitive personal data unless the platform’s data handling policies clearly permit it and we have explicit consent.
  • Keep internal summaries within the organization and restrict who can trigger more detailed content generation.

These steps reduce risk and keep our customers’ data safe while still leveraging the power of the models.

Testing and measuring success

We should measure both qualitative and quantitative outcomes to judge the success of our AI workflows:

  • Qualitative: Did responses feel human and helpful? Did the AI reduce back-and-forth with prospects?
  • Quantitative: Track time saved per task, response times, conversion rates of AI-qualified leads, and token usage costs per automation.

Start small: run A/B tests comparing human vs AI-first interactions, and scale the workflows that demonstrate improved conversion rates or reduced manual effort.

Sample workflow templates we can implement today

Below are three simple workflow templates that are easy to set up and provide immediate value.

1. Quick lead qualification flow (Nano)

  1. Trigger: New lead form submission.
  2. Action: Send introductory message asking 2–3 qualifying questions (budget, timeline, primary need) using Nano.
  3. Action: If lead answers meet qualification thresholds, notify sales with a summarized note; otherwise, place lead in nurture sequence.

2. Post-demo follow-up (Mini)

  1. Trigger: Demo completed tag added to contact.
  2. Action: Generate a personalized follow-up email summarizing what was discussed and next steps (Mini).
  3. Action: Send email and create a task for sales to reach out if there is no reply within 3 days.

3. Content generation with human review (Full GPT-5)

  1. Trigger: Request to create an in-depth article or report.
  2. Action: Generate first draft with full GPT-5 and limit output length to control cost.
  3. Action: Assign draft to a content editor for review and publish after approval.

How we balance automation and the human touch

AI should augment our team, not replace meaningful human interactions. We use automation to handle the routine and leave humans to do high-touch selling and relationship building. By routing complex or sensitive conversations to people, and using models for qualification, follow-ups, and drafting, we reduce workload without sacrificing quality.

In practice, that means using Nano for fast responses, Mini for polished templates and personalization, and GPT-5 full for nuanced content that requires human-level reasoning. It’s a triage approach that protects quality and controls costs.

What to watch for in the coming months

Language models and workflow tools will keep improving. We expect enhancements in contextual memory, better cost-optimization tools inside workflow editors, and more granular controls for token usage. Keeping our automations modular — so we can swap model tiers per step — will let us take advantage of improvements as they arrive.

FAQ

Which model should we choose for chatbot replies?

For chatbots and quick conversational replies, we recommend Nano. It’s optimized for speed and cost while providing conversational quality that is often indistinguishable from higher-tier models for short interactions.

How do tokens translate to real-world costs?

Tokens are small pieces of text; one token is about 0.75 of a word. All pricing is per million tokens for both inputs and outputs. Short prompts and short responses consume very few tokens, while long prompts and detailed outputs can raise costs significantly. Use model selection and max token limits to manage spend.

Should we always prefer the cheapest model?

Not always. Use the cheapest model that meets the task’s standards. For short conversational tasks, Nano is perfect. For structured templates or multi-step reasoning, Mini may be more appropriate. Reserve the full model for complex reasoning, long-form content, or tasks where accuracy and nuance matter.

How can we keep our costs predictable?

Default to Nano or Mini where possible, set max output lengths, avoid passing unnecessary context, and monitor token usage. Consider batching expensive tasks and adding a human review step to reduce redundant outputs.

Is there any difference in the type of errors produced by different models?

Higher-tier models tend to provide more coherent and contextually accurate outputs for complex queries. Lower-tier models may occasionally oversimplify, ask clarification questions, or produce shorter responses. Always test responses in realistic scenarios before deploying widely.

How can we test which model is best for a given workflow?

Run comparative tests using the same prompt across all three models and evaluate outputs for quality, speed, and cost. Track performance metrics like lead conversion, response time, and token usage to make data-driven decisions.

Conclusion

We’re excited about the flexibility this update brings. By adding three tiers of ChatGPT 5 models directly into our workflow editor, we can match intelligence and cost to the needs of each automation. The full GPT-5 model gives us deep reasoning and long-form generation; Mini offers a balanced blend of capability and cost; Nano delivers fast, inexpensive conversational responses ideal for chatbots and CRM tasks.

To get started, we recommend testing the three models on a few representative automations: a chatbot flow, a follow-up sequence, and a content generation task. Compare outputs, measure token usage, and iterate on prompts and settings. With careful model selection and prompt design, we can drastically reduce manual workload, speed up response times, and keep costs transparent and predictable.

We want to thank Andrew George for the practical demonstration that inspired this deeper look. Let’s take what we learned, run a few tests in our workflow editor, and start creating smarter, faster automations that let our teams focus on what matters most — building relationships and growing the business.

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