Eval Driven Development for Engineers
Helping Software Engineers Turn AI Prototypes into Production-Ready Systems | Live and Hands-On Workshop
Why This Matters Now
AI is no longer confined to prototypes. It is being integrated into products, workflows, and decision-making systems across industries. As usage grows, so does the impact of failure. Inconsistent outputs, silent regressions, and hallucinations are not edge cases anymore. They are operational risks.
Without a systematic way to evaluate and monitor these systems, teams are left guessing. Eval-Driven Development brings clarity and discipline, making reliability measurable and repeatable rather than subjective.
What You Will Learn
- How to move from subjective “vibe-based” testing to a structured Eval-Driven Development (EDD) approach for AI systems
- Define correctness using semantic evaluation instead of simple string matching
- Build and maintain a “Golden Dataset” with real-world scenarios, edge cases, and production context
- Design and implement an LLM-as-Judge for scalable, automated evaluation
- Create scoring rubrics and calibrate them against human judgment
- Integrate evaluation into CI/CD pipelines with clear release gates and regression testing
- Manage cost, latency, and reliability trade-offs in production systems
- Perform debugging and root cause analysis for failures in probabilistic systems
- Build a production readiness gate that prevents unreliable AI outputs from going live
What You’ll Walk Away With
- A practical framework for building and maintaining reliable AI systems
- A working evaluation pipeline you can adapt to your own use cases
- A structured dataset that defines correctness for your application
- A repeatable process for testing, debugging, and improving system performance
- A production readiness approach that reduces risk before deployment
What Makes This Different
This is not a workshop about prompt tips or model comparisons. It focuses on how to build systems that behave reliably over time.
- It treats evaluation as a core part of development, not an afterthought
- It emphasizes real-world workflows over isolated examples
- It focuses on debugging and failure analysis, not just output quality
- It connects AI development with established software engineering practices
- It is designed for teams that need to ship and maintain systems, not just experiment
Who Should Attend
- Software engineers and systems engineers building AI-powered or LLM-based applications
- AI/ML engineers and practitioners working on reliability, evaluation, or deployment of models
- Technical leads and architects responsible for production-grade AI systems
- Product engineers integrating LLMs into user-facing features
- Teams transitioning from prototyping to production with AI systems
- Anyone frustrated with unreliable outputs and looking for a systematic, test-driven approach to AI development
Limited Seats. High Impact.
This is a live, interactive workshop with limited seats to maintain quality and hands-on depth.
Helping Software Engineers Turn AI Prototypes into Production-Ready Systems | Live and Hands-On Workshop
Why This Matters Now
AI is no longer confined to prototypes. It is being integrated into products, workflows, and decision-making systems across industries. As usage grows, so does the impact of failure. Inconsistent outputs, silent regressions, and hallucinations are not edge cases anymore. They are operational risks.
Without a systematic way to evaluate and monitor these systems, teams are left guessing. Eval-Driven Development brings clarity and discipline, making reliability measurable and repeatable rather than subjective.
What You Will Learn
- How to move from subjective “vibe-based” testing to a structured Eval-Driven Development (EDD) approach for AI systems
- Define correctness using semantic evaluation instead of simple string matching
- Build and maintain a “Golden Dataset” with real-world scenarios, edge cases, and production context
- Design and implement an LLM-as-Judge for scalable, automated evaluation
- Create scoring rubrics and calibrate them against human judgment
- Integrate evaluation into CI/CD pipelines with clear release gates and regression testing
- Manage cost, latency, and reliability trade-offs in production systems
- Perform debugging and root cause analysis for failures in probabilistic systems
- Build a production readiness gate that prevents unreliable AI outputs from going live
What You’ll Walk Away With
- A practical framework for building and maintaining reliable AI systems
- A working evaluation pipeline you can adapt to your own use cases
- A structured dataset that defines correctness for your application
- A repeatable process for testing, debugging, and improving system performance
- A production readiness approach that reduces risk before deployment
What Makes This Different
This is not a workshop about prompt tips or model comparisons. It focuses on how to build systems that behave reliably over time.
- It treats evaluation as a core part of development, not an afterthought
- It emphasizes real-world workflows over isolated examples
- It focuses on debugging and failure analysis, not just output quality
- It connects AI development with established software engineering practices
- It is designed for teams that need to ship and maintain systems, not just experiment
Who Should Attend
- Software engineers and systems engineers building AI-powered or LLM-based applications
- AI/ML engineers and practitioners working on reliability, evaluation, or deployment of models
- Technical leads and architects responsible for production-grade AI systems
- Product engineers integrating LLMs into user-facing features
- Teams transitioning from prototyping to production with AI systems
- Anyone frustrated with unreliable outputs and looking for a systematic, test-driven approach to AI development
Limited Seats. High Impact.
This is a live, interactive workshop with limited seats to maintain quality and hands-on depth.
Lineup
Imran Ahmad
Good to know
Highlights
- 5 hours
- Online
Refund Policy
Location
Online event
Agenda
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Session 1: Building the “Golden Dataset”
Before improving outputs, you need a clear definition of what “good” looks like. In this module, participants will focus on creating a high-quality “Golden Dataset” that reflects real-world usage. This includes designing examples that go beyond simple inputs to capture edge cases, ambiguity, and production context. You will also learn how to evaluate outputs based on meaning and intent rather than exact matches, and how to maintain and version your dataset as requirements evolve.
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Session 2: Architecting the “LLM-as-Judge”
Manual evaluation quickly becomes a bottleneck. This module introduces a scalable alternative by building an automated evaluation layer using an LLM as a judge. You will design structured scoring rubrics that assess dimensions like accuracy, tone, and tool usage. The focus is not just on automation, but on reliability. You will learn how to calibrate your evaluation system against human judgment and identify where human review is still necessary.
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Session 3: The “Green-Red” Loop and CI Integration
Reliable systems require continuous testing, not one-time validation. In this module, you will integrate evaluation into a development workflow that mirrors modern software practices. This includes defining performance thresholds for release decisions, setting up regression tests to catch unintended changes, and incorporating evaluation checks into CI pipelines. You will also explore how to balance evaluation depth with cost and latency constraints in real-world environments.