Practical AI Insights from the Trenches¶

The Secret to Better LLM Outputs: Multiple Structured Reasoning Steps

Traditional chain-of-thought prompting is leaving performance on the table.

While working on a recent client project, we A/B tested different prompting approaches.

Breaking LLM reasoning into multiple structured steps was preferred 80% of the time over traditional methods.

Instead of one meandering thought stream, we can greatly boost reliability now get precise by using a tightly controlled response model

• Analyze the example structure
• Analyze the example style
• Generate the output based on the previous steps

I'll show you exactly how to implement this approach using the Instructor library, with real examples you can use today.

You Don't Need to Fine-Tune to Clone YOUR Report Style

"This doesn't sound like us at all."

It's the all-too-familiar frustration when organizations try using AI to generate reports and documentation.

While AI can produce grammatically perfect content, it often fails at the crucial task of matching an organization's voice - turning what should be a productivity boost into a major bottleneck.

I'll show you how we solved this using a novel two-step approach that separates style from data.

By breaking down what seemed like an AI fine-tuning problem into a careful prompt engineering solution, we achieved something remarkable:

AI-generated reports that practitioners couldn't distinguish from their own writing.

Here's what we delivered:

• Style matching so accurate that practitioners consistently approved the outputs
• Complete elimination of data contamination from example reports
• A solution that scales effortlessly from 10 to 1000 users
• Zero need for expensive fine-tuning or ML expertise

Best of all? You can implement this approach yourself using prompt engineering alone - no complex ML infrastructure required.