A Week, an Idea, and an AI Evaluation System: What I Learned Along the Way

How the Project Started

I remember the moment the evaluation request landed in my Slack. The excitement was palpable—a chance to delve into a challenge that was rarely explored.
The goal? To create a system that could evaluate the performance of human agents during conversations.

It felt like embarking on a treasure hunt, armed with nothing but a week’s worth of time and a wild idea.
Little did I know, this project would not only test my technical skills but also push the boundaries of what I thought was possible in AI evaluation.

A Rarely Explored Problem Space

Conversations are nuanced; they’re filled with emotions, tones, and subtle cues that a machine often struggles to decipher.
This project was an opportunity to explore a domain that needed attention—a chance to bridge the gap between human conversation and machine understanding.

What Needed to Be Built

With the clock ticking, the mission was clear:

• Create a conversation evaluation framework capable of scoring AI agents based on predefined criteria.
• Provide evidence of performance to build trust in the evaluation.
• Ensure that the system could adapt to various conversational styles and tones.

What made this mission so thrilling was the challenge of designing a system that could accurately evaluate the intricacies of human dialogue—all within just one week.

What Made the Work Hard (and Exciting)

This project was both daunting and exhilarating. I was tasked with:

• Understanding the nuances of human conversation: How do you capture the essence of a chat filled with sarcasm or hesitation?
• Developing a scoring rubric: A clear, structured approach was essential to avoid ambiguity in evaluations.
• Iterating quickly: With a week-long deadline, every hour counted, and fast feedback loops became my best friends.

Despite the challenges, the thrill of creating something groundbreaking kept me motivated.
The feeling of building something new always excites me—it’s unpredictable, and there was always a chance the entire system could fail.

Lessons Learned While Building the Evaluation Framework

Through the highs and lows of this intense week, I gleaned valuable insights worth sharing:

• Quality isn’t an afterthought—it's a system. Reliable evaluation requires clear rubrics, structured scoring, and consistent measurement rules that remove ambiguity.
• Human nuance is harder than model logic. Real conversations involve tone shifts, emotions, sarcasm, hesitation, filler words, incomplete sentences, and even transcription errors. Teaching AI to interpret this required deeper work than expected.
• Criteria must be precise or the AI will drift. Vague rubrics lead to inconsistent scoring. Human expectations must be translated into measurable and testable standards.
• Evidence-based scoring builds trust. It wasn’t enough for the system to assign a score—we had to show why. High-quality evidence extraction became a core pillar.
• Evaluation is iterative. Early versions seemed “okay” until real conversations exposed blind spots. Each iteration sharpened accuracy and generalization.
• Edge cases are the real teachers. Background noise, overlapping speakers, low empathy moments, escalations, or long pauses forced the system to become more robust.
• Time pressure forces clarity. With only a week, prioritization and fast feedback loops became essential. The constraint was ultimately a strength.
• A good evaluation system becomes a product. What began as a one-week sprint became one of our most popular services because quality, clarity, and trust are universal needs.

How the System Works (High-Level Overview)

The evaluation system operates on a multi-faceted, evidence-based approach:

1. Data Collection: Conversations are transcribed and analyzed in over 60 languages.

2. Evaluation on Rubrics: The AI evaluates transcripts against structured sub-criteria using our Evaluation Data Model.

3. Scoring Mechanism: Each criterion is scored out of 100, with weighted sub-criteria and supporting evidence.

4. Performance Summary & Breakdown:

   • Overall summary
   • Detailed score breakdown
   • Relevant quotes from the conversation
   • Evidence that supports each evaluation

This approach streamlines evaluation and empowers teams to make faster, more informed decisions.

Real Impact — How Teams Use It

Since launching, teams across product, sales, customer experience, and research have leveraged the evaluation system to enhance their operations.

They are now able to:

• Identify strengths and weaknesses in AI interactions.
• Provide targeted training to improve agent performance.
• Foster a culture of continuous, evidence-driven improvement.

The real impact lies in transforming conversations into actionable insights—leading to better customer experiences and stronger business outcomes.

Conclusion — From One-Week Sprint to Flagship Product

What started as a one-week sprint has now evolved into a flagship product that continues to grow and adapt.

This journey taught me that the intersection of human conversation and AI evaluation is not just a technical pursuit—it’s about understanding the essence of communication itself.

“I build intelligent systems that help humans make sense of data, discover insights, and act smarter.”

This project became a living embodiment of that philosophy.

By refining the evaluation framework, addressing the nuances of human conversation, and focusing on evidence-based scoring, we created a robust system that not only meets our needs but also sets a new industry standard for AI evaluation.