Managing social networks takes a lot of time, even when using ChatGPT and the like. I met Antonino Crisà, entrepreneur and community manager, who was spending more time than he wanted producing content for his clients.
In 2025, many professionals like Antonino are genuinely disappointed by AI. The initial amazement and productivity gains on simple tasks give way to a harsher reality: for complex work or high volumes, manual labor is back in force.
And when it comes to social networks, the self-respecting professional has no room for error. We’re all familiar with the lack of originality of posts foolishly generated with ChatGPT.
Here’s how I helped him use an artificial intelligence workflow to produce more, faster, and in line with his customers’ needs.
What is an AI workflow and how does it work?
An artificial intelligence workflow is a chain of steps in which different AI models or assistants work together to accomplish a complex task.
Unlike a single model like ChatGPT, which tries to do everything, each workflow component is specialized in a specific task. Information flows automatically from one step to the next. It’s like a well-oiled factory, where each workstation excels in its specialty.
Imagine one assistant specializing in tweet generation, another in thread generation, another in business intelligence, or yet another in visual production, all working towards a common goal.
It’s simply a chain of events, with each part doing a specific job.
Solutions such as Zapier, Make, aimw and n8n let you do just that. A AI consultant consultant can help you in this process.
Why simple GPTs or chatbots are not enough
My client was using OpenAI’s GPTs to create social media content. But three major problems arose that are common to simple chatbots and that you’ve probably already experienced:
- Instructions evaporated – GPTs regularly “forgot” their instructions
- Endless copy-and-paste – Constant manual navigation between OpenAI, fal.ai and Kling AI (for visual production)
- Inconsistent results – Variable quality from day to day, requiring constant updates
In short: an inefficient process that was costing him precious hours every day.
Overview of the workflow I’ve created
- Chatbot entry – Simple starting point for the consultant
- Algorithm monitoring module – Keeps up to date with platform developments
- Intelligent router – Directs requests to specialized branches (LinkedIn post, Tweet, comment…)
- Text content generators – Create messages tailored to each platform
- Visual prompt generators – Transform text into instructions for images
- Image APIs – Communicate with visual generation services
- Video API – Create animations if required
- Chatbot output or post directly to networks – Present final results or post directly to the social network
The consultant simply enters what he wants, for whom, and in what form. The system does the rest.
The two main steps to a high-performance workflow
1. Creating an AI assistant familiar with the customer’s context
For each customer, I’ve created ultra-precise instructions (but very easy to reproduce for your business) that define :
- The exact tone of voice (formal, relaxed, technical, humorous…)
- Target audiences and their characteristics
- Specific terminology to be used
- List of products and services, and customer positioning
- A dozen examples of successful posts
- Rules, notes, exceptions
- Etc.
Unlike GPTs, these instructions remain engraved in the system. No forgetting, no drifting. The result: content perfectly consistent with each brand’s identity.
My instructions are thousands of words long. Of course, I didn’t write them all by hand. I asked Antonino to share with me the various elements he had available for his customers, grouped them together, analyzed them, extracted them, and asked an AI to format clear, detailed instructions for me.
In fact, it’s very easy. Ask me or the AI what instruction structure is appropriate for your specific purpose. Then gather all the documents in your company that might be useful for contextualizing its instructions in textual form. Copy and paste this raw document into your chatbot, and ask it to integrate your context within the instructions. And that’s it!
2. Workflow logic
This is the most complex task. Where to start? What to pass from one assistant to the next? In what order? How will this affect the creativity or quality of the final content?
Let’s take the example of a “tweet” request to see how I’ve structured the workflow:
- Simple user input – Antonino enters only the subject of the tweet, its purpose, and whether he wants an image or video. For example: “Tweet about our new automation feature to attract leads, with a tech image”.
- Algorithmic monitoring – A specialized module checks the latest trends and updates from X/Twitter to ensure that content follows the practices that currently generate the most engagement.
- Customized tweets – The module generates several variations of tweets that respect the customer’s tone of voice, naturally integrate their products, and align with their marketing strategy.
- Production of the visual prompt – A module transforms the tweet content into precise visual instructions, associating key concepts with the customer’s graphic charter.
- Image generation via API – These instructions are sent automatically to Replicate, which runs a template like Flux Pro to create a professional image, without any copy and paste.
- Conditional decision – The workflow checks whether a video is required or whether the static image is sufficient, executing only those steps that are really useful.
- Video animation (if requested) – If required, the image is transmitted to Kling AI, which animates it naturally to maximize engagement.
- Centralized delivery – All elements (tweets, image, video) are presented in a single chatbot, with publishing advice based on current data.
Why this workflow is superior to conversational AI assistants
The fundamental difference with OpenAI’s GPTs lies in the architecture of the system itself. Where conversational assistants can “forget” or shuffle their instructions over the course of a session, my workflow maintains permanent instructions at every stage. Rather than asking a single model to perform all tasks, I use specialized modules precisely optimized for each specific function.
Technical integration is another major advantage. Whereas GPTs require a lot of manual intervention between different tools, my workflow fully automates information transfers. What’s more, it completely isolates each customer in its own environment, eliminating the risk of confusion between different brands.
These structural differences translate into considerable time savings and a much higher quality of result.
Want to automate your social networks? Please contact me: clement@clementschneider.ai
