Why your LLM is building a car when you asked for a pterodactyl

I recently returned from Chicago where I earned my certification in Optimizely Opal Development. Spending the day diving deep into AI workflows with experts like Remko Jantzen (LinkedIn) sparked a major realization about why so many AI implementations fail to be production ready.

The Problem: The Lego Bucket

We all know LLMs are powerful, but they can be prone to hilariously impressive hallucinations. Relying on human monitoring to catch these errors defeats the purpose of automation and is often a trap (see Bainbridge’s "Ironies of Automation," 1983).

Think of a basic LLM prompt like handing a kid a random bucket of Legos and saying, "Build me a pterodactyl."

Best case scenario? You hand the bucket to a wunderkind who creates a masterpiece that challenges the foundational assumptions of paleontology. But this is a fluke, a bit of intermittent positive reinforcement that tricks us into thinking we just need to write one more prompt.

The more likely scenario? You get back a weird bird-plane hybrid, or the kid gets distracted by the cool wheel pieces and builds a car instead.

The Fix: Building the Pterodactyl Brick by Brick

The secret to reliability isn’t "better monitoring" - it’s better, granular architecture. In Optimizely Opal, we solve this by treating prompts like code:

Workflow Agents (The Instruction Booklet)

These agents daisy-chain the specialists together. They don’t do the work; they ensure the "Lego steps" are followed in order.

Specialized Agents (The Microservices)

Instead of one massive, all-encompassing prompt, we break tasks down. Just as developers hate functions with side effects, we should hate bloated Agents. By using granular specialized agents, we get:

• Better Focus: They accomplish narrowly scoped tasks reliably.
• Reusability: Agents can be recycled into other workflows.
• Simplicity: Easier to debug and understand.
• Tooling: They can access specific tools (web browsing, external integrations) required for that specific task.
• Cost Efficiency: No need to invoke an expensive "thinking" model when a faster, cheaper model can handle a simple task.

Frameworks

You get consistent results when you utilize structured frameworks rather than open-ended queries. From the workshop, the one that stands out most was CLEAR (Context, Logic, Examples, Action, Refinement) which enables you to define what the output looks like and put guardrails around it.

Our Experience

My colleagues Shahira (LinkedIn), Lance (LinkedIn), and I recently built an Optimizely Hackathon solution that utilized specialized agents to scrape a site, perform competitive market analysis, and generate a persuasive llms.txt file to publish back to the CMS.

Looking back, I wish we had taken this workshop before the hackathon! These principles and frameworks would have brought a lot more clarity and consistency to our submission, bringing it significantly closer to "production-ready," though I’m still pleased with what we were able to submit.

Excited to see where we can take this technology next!

#OptimizelyOpal #AI #PromptEngineering #WorkflowAutomation #Optimizely #TechTrends #Nishtech