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How important is it to inform and train employees? There is not always enough time, and it is not always the first activity to which budget is allocated.
I came up with this workflow to generate an AI-enhanced corporate trainer. Starting from the definition of the brand and the topic to be addressed, through LLM nodes, the brand’s vision and mission are analyzed, the characteristics of a hypothetical trainer are defined, a long-form text on the topic is generated, followed by a teaser video script for the course, the corresponding audio, and finally an animated video of the trainer presenting the generated content.
Depending on the subscription plan and the amount of credits available, my ideal scenario would then allow for the generation of the entire series of content.
P.S. The potential of the result could have been incredibly high, but I encountered numerous bugs in the application, although I am not sure whether they depend on cache issues or other factors, as well as some limitations, such as: interconnected nodes generating previous results twice; the impossibility of leaving the voice randomized; when mixing video and audio, the inability to trim the music track if it is longer than the video; credit management often being insufficient; the flow publication process not being immediately understandable; and the mandatory generation required for publication being very expensive, yet not clearly disclosed, creating a barrier to completing the process... These resulted into a woman HR with male voice, but the first time I generated it was correct, I have used free credits but weren't sufficient for the testing phase, so I swtitched to an other account with paid subscription, but in the end I'm not totally statisfied with the result, thinking about the idea I had in mind...
elevenlabs.io
CORPORATE TRAINER VIDEO COURSE | ElevenCreative Templates
This flow was born from the desire to provide a useful tool for employeea training, significantly reducing development times and costs. Choose a brand and a theme and develope a course for your employees i. The idea is that you can start by the analysis of the vision and the mission of THE BRAND that can be adjusted case to case. ii. Then the result is converted into physical characteristics of a possible corporate trainer iii. In parallel you can define the theme of the course: right now is set to be able, starting from the definition of a theme, to generate a long text for the course (in alternative if a text is internally already done, can be copied and pasted to generate the speech) Depending on the subscription and credits available, you can be able to create the whole video or the tteaser, right now I have proceeded with the teaser Only iv. in fact there is the space to generate a short text for the teaser, that can be converted into speech, and lip sync with the starting image of the corporate trainer
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They can read documents, extract data, check missing details, update CRMs, send notifications, and prepare tasks for human review.
For example, when a client submits a form or document, an AI workflow can extract the key information, validate it, update the right tools, and notify the team automatically.
The goal is not to replace people. It is to remove repetitive work so teams can focus on better decisions.
The best AI automation keeps humans in the loop, adds error handling, and connects tools through APIs, webhooks, Slack, Google Sheets, CRMs, and workflow platforms like n8n.
Less manual work. Faster handoffs. Better operations.
AI is a great copilot, but it’s a terrible captain.
With AI coding assistants, it’s easy to fall into the trap of letting the LLM drive the entire development workflow. You prompt, copy, paste, and pray.
But our value as software engineers has never been about how fast we type syntax. It’s about system design, critical thinking, and code ownership.
Our role is evolving from builders to technical pilots. If you completely delegate the workflow, you lose control of the project.
Here is a quick framework to keep ownership of your features while leveraging AI:
🛠️ The 4-Step Workflow Guide
Design First, Prompt Second: Define your architecture, data flow, and edge cases before touching an LLM. Use AI to implement, not to invent.
Deconstruct: Break features into micro-steps. Guide the AI through isolated, high-context tasks (schema ➡️ logic ➡️ error handling).
Radical Skepticism: Treat AI code like a PR from an intern who is incredibly fast but occasionally hallucinates. Test and review every single line.
Own the "Why": If a bug hits production, "the AI wrote it" is not an option. You are responsible for the code you ship.
📌 To Developers: The future belongs to those who use AI to accelerate execution, but keep their eyes on system architecture and product sense.
📌 To Tech Recruiters: Stop looking for syntax experts. Look for developers with strong debugging frameworks, system-level thinking, and true project ownership.
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