AI Iteration: Why your AI-model (Almost) Never Works on the First Try

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Why AI (Almost) Never Works on the First Try — and What to Do Instead

If you have ever typed “just one more try” into ChatGPT and still weren’t fully satisfied after twenty rounds: you’re not imagining things — this is how it works. In a detailed longread, Fluxus CEO Django Beatty explains why generative AI rarely gets it right the first time: models “walk” word by word without being able to plan ahead. This means improving in rounds is not a flaw, but a natural feature of the system.

He even calls it Beatty’s Law: you never know if you are one or twenty improvement rounds (also called iterations) away from a good result. He also introduces a useful image: AI as a cognitive exoskeleton — you are the pilot, the machine amplifies your thinking but still needs your direction (just as we need Google Maps to reach our destination).

In this blog, we translate those insights into practical choices for teams that want to use AI seriously — focusing on quality, transparency, and efficiency, exactly the values we prioritize at Lumans.

What This Means for Your Organization

Improvement rounds are inevitable — plan for them. Think of AI work as navigating without a map: start with a rough direction, then adjust based on feedback and errors. This is why “good prompting” works: it gets the model closer to the destination and reduces the number of improvement rounds needed.
Be deliberate about when to push further and when “good enough” is fine. Beatty’s advice is simple and valuable: spend extra care and time on tasks where excellence matters (production code, client-facing content); accept “good enough for now” for internal notes or draft outlines.
You remain the guide. AI does not replace you; your role shifts to director/quality controller. Always keep human oversight and perform a final check every time: check, check, and check again.

A Compact AI Improvement Plan (That Actually Works)

Step 1 — Define “what is excellent?” Write or decide on clear acceptance criteria and tone/style guidelines. Without a goal, there’s no direction. (Pro tip: give the model a role and context — it reduces the search space.)

Step 2 — Start with a small, well-defined piece. Begin with a mini process (e.g., just the introduction of a proposal). Go through a few improvement rounds quickly and collect examples of good vs. less good output.

Step 3 — Build a small test set (10–30 examples). Run the system against the same benchmark each time. This way you can see if rounds actually improve results instead of just “feeling” like they do.

Step 4 — Use a fixed prompt structure. Follow a simple template: role → context → task → constraints, and add what it must not do.

Step 5 — Standardize human checks along the way. Decide who checks when, where you declare something “good enough” (step 1), and when you escalate to more rounds.

Step 6 — Document and share what works. Save working prompts/snippets as team assets. This speeds up quality improvements and reduces skill gaps between colleagues. (Yes, this also works for brainstorming and breaking through creative blocks.)

Common Pitfalls (and What to Do Instead)

“We write even longer prompts.” More words ≠ better direction. Clarity and constraints beat volume.

“We stop after the first wow.” The first version is usually average. Push through a few more rounds until the result really stands out.

“We skip the final check.” AI is still just a tool; you are responsible for the final quality.

Why This Fits the Way Lumans Works

We help teams tackle exactly these challenges in practical ways. In a short, hands-on workshop you will develop:

Practical know-how for AI workflows
A compact prompt playbook (role → context → task → constraints)
A clear view of AI’s potential in your daily work

With this approach, we have already helped many professionals work more consciously with both the opportunities and the risks of AI. Call or message us for more information!

Get Started Today — Exercises You Can Try Now

Choose one task where quality matters (e.g., client email, proposal intro) and one where “okay” is enough (e.g., internal meeting notes). Apply the “push where it matters” principle.
Write a prompt in four parts (role, context, task, constraints) and save it in your team wiki. Need extra guidance? Send us a message and we will be happy to help you or your team through a workshop!
Start testing. If you discover something useful, save it — and maybe even build your own GPT or similar tool.

In short: AI rarely works perfectly in one go — and it doesn’t have to. If you organize improvement rounds, AI becomes a powerful exoskeleton for your team instead of a slot machine.

Curious about what this means for your organisation? We offer AI consultancy, workshops, and custom software. Learn more about AI’s potential for your business at lumans.ai.