Three years of generative AI: 6 lessons and 1 certainty, the power of the method

Since 2022, we have been immersed in the generative AI ecosystem. In this time, we have burned more credits than an investor in Dogecoin (pardon the joke about cryptobros)... But, after much trial and error, we have reached some interesting conclusions and discovered golden tricks.

We tell you everything in this article which, wink-wink, has not been written using ChatGPT. This way, you save tokens, hours of rendering, and tons of frustration. How nice we are at Fail Fast.

Lesson #1: Pre-production is a contract.

Trusting that AI "solves everything" sadly ends up taking us down the most expensive route. The quality of our output will directly depend on the precision with which the task has been designed from the beginning. It works, so to speak, to conceive pre-production as a commitment or contract.

⚠️ We recommend: Generate detailed scripts, storyboards, and technical sheets. Define shot lists and moodboards. Delimit key technical specifications, such as lens types, shot types, color palettes... And, above all, which ones to avoid.

Following this guideline, we have reduced blind iterations in our projects and have cut costs and rendering times by up to 40%.

Lesson #2: Each model speaks its own language

Using the same prompt on all platforms is a common and costly mistake. Each one applies its own ways, codes, and even biases to interpret your instructions.

For example, the more artistic prompting that Midjourney understands is not useful in Stable Diffusion, which prefers something more technical... Not to mention what happens with Sora or Pika.

☝️ We advise: Spend some time learning the specific language of each tool. Adjust structure, token weight, and terminology. Otherwise, you will get abstract art even if you asked for hyperrealism.

Lesson #3: Serendipity, yes; magic, no.

A clear roadmap is vital, but generative AI also shines as a tool for discovery. The key: the balance between your objective and the randomness of the algorithm.

👀 We suggest: Maintain your plan, your North Star, as a base, but leave room for creativity. That is, be strict about the what, but flexible about the how. For example, if you need a cutaway shot, limit emotions and atmosphere, but let the AI surprise you with unexpected compositions and viewpoints.

Lesson #4: Technical Possibilism

The excitement generated by a demo quickly deflates when the technical limitations of the different tools are checked. Before promising, evaluate risks.

🗣️ Our advice: List the potential dangers: object/world consistency, image flickering, movement deformations, impossible actions... Keep in mind that long shots and dynamic environments (water, smoke, fire) add complexity.

Ask yourself a question: is the tool capable of maintaining the consistency of the character, object, or environment for several seconds? If you doubt it, increase the margin for human correction.

Lesson #5: AI + Post-production, the infallible combination

Obtaining the perfect image directly from the prompt is still a utopia: it is usually a slow, costly, frustrating path... Regenerate until it comes out? No! Only the final human touch will achieve the finish you are looking for.

🤔 We suggest: Adopt a pro-tip workflow: generate by layers, elements, or light passes and adjust every detail with more conventional software or tools, from Photoshop to chroma keys. This way, you ensure greater control, fewer errors, and more consistent continuity between shots.

Lesson #6: Your margin should not dance to the rhythm of Saas

It's a matter of strategy: your business cannot be 100% dependent on a Software as a Service provider.

The reason? Platforms change their pricing policies, usage limits, or even their API without prior notice. And that will diminish your profit.

👂 We propose: Build your own internal pipeline so that you have the technical capacity and knowledge to pivot quickly to fine-tuned Open Source models or models hosted in your own infrastructure. Diversification is resilience.

In Conclusion

Generative AI does not eliminate work, it transforms it. Of course, it requires hyper-planning and impeccable technical execution.

‼️ Remember: tokens cost money, but the loss of time and frustration undermine the well-being of your team.

At Fail Fast, we are committed to sharing our experience (and our missteps, always enriching) to accelerate everyone's success.

🫵 Question for you: Of these 6 lessons, point 4 usually carries the greatest risk of loss. Which one has cost you the most credits or time in your last project? Tell us in our social networks.

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