Phase 1: Building and Iteratively Improving Your GenAI App

This phase covers the initial development of your GenAI application, from building your first proof-of-concept through iterative improvement based on feedback from test users. You'll establish the foundation for systematic development, debugging, and quality improvement that will serve you throughout your application's lifecycle.

Table of Contents

• Overview
• Challenge 1: Debug Application Logic
• Challenge 2: Leverage Test User Queries
• Challenge 3: Track Quality Feedback
• Challenge 4: Systematic Prompt Iteration
• Phase 1 Summary

Overview

During Phase 1, you'll face several key challenges as you develop and refine your GenAI application:

Challenge	Solution	Key Benefit
Debug Complex Logic	MLflow Tracing	Complete observability across development and production
Use Real User Queries	Trace Collection & Search	Test with realistic usage patterns
Track Quality Feedback	Feedback APIs & UI	Systematic quality assessment
Get Expert Input	Review App Integration	Authoritative validation from domain experts
Iterate on Prompts	Evaluation Harness	Side-by-side comparison of variants

Challenge 1: Debug Application Logic

The Problem

Even simple applications with a single prompt can be difficult to debug. As your app grows beyond basic prompts to include external data sources like vector databases and APIs, debugging becomes increasingly complex. You need to understand what input data was used for each variable in your prompt template, determine whether latency issues stem from the LLM or a slow API call, reproduce user issues by understanding exactly how their input was processed through each step, and maintain the same debugging visibility in both local development and production environments.

Solution: Implement MLflow Tracing

MLflow Tracing provides complete observability into your application's logic, latency, and cost. The same tracing works identically in development and production environments.

Implementation Benefits

Beyond immediate debugging capabilities, implementing tracing enables future capabilities including quality feedback attachment to specific execution traces, performance optimization through detailed timing analysis, cost tracking across different configurations, and production monitoring with the same observability tools.

Challenge 2: Leverage Test User Queries

The Problem

During development, beta testers provide valuable real-world examples that reveal natural language patterns showing how users actually phrase questions, realistic usage scenarios you hadn't considered, quality gaps where your application underperforms, and edge cases involving unusual but valid user inputs. You need a systematic way to capture and reuse these examples for testing improvements.

Solution: Systematic Query Collection

MLflow Tracing automatically captures every user interaction with complete context. Use the MLflow Trace UI and SDK to browse, select, and reuse valuable examples.

Query Collection Workflow

Automatic capture records all user interactions during beta testing with rich context including inputs, outputs, timing, and metadata. Programmatic access via the search_traces SDK enables notebook and IDE integration for easy exploration. Direct reuse allows you to leverage trace.inputs for validation testing, making it simple to test improvements against real user queries that previously caused issues or represented important use cases.

The systematic approach ensures you're testing with realistic usage patterns rather than synthetic examples, capturing the natural language variations that users actually employ, and building a comprehensive library of test cases that grows with your application.

Challenge 3: Track Quality Feedback

The Problem

Even with a small group of test users, you need reliable ways to capture user feedback on response quality, log your own observations during development iteration, identify problem patterns across different types of queries, and guide improvement priorities based on systematic feedback collection. Without structured feedback collection, quality issues can go unnoticed or be difficult to reproduce and fix.

Solution: Integrated Feedback Collection

MLflow's feedback system allows you to attach quality assessments directly to traces through multiple channels.

Feedback Collection Methods

The system supports multiple feedback collection approaches to accommodate different workflows and user types. MLflow Trace UI allows direct annotation of traces during development, perfect for developers who want to mark specific interactions for further analysis.

This multi-channel approach ensures that feedback collection doesn't become a bottleneck while maintaining the structure needed for systematic quality analysis.

Challenge 4: Systematic Prompt Iteration

The Problem

When iterating on prompts and code changes to improve quality, you need systematic testing of multiple variations against the same inputs, side-by-side comparison to quickly identify the best performing version, version tracking to promote successful changes to production, debugging capability to understand why specific versions succeeded or failed, and expert validation when needed to confirm quality improvements. Manual testing of variations is time-consuming and error-prone.

Solution: MLflow Evaluation Harness

The Evaluation Harness enables systematic testing and comparison of multiple application variants with minimal setup effort.

Systematic Evaluation Process

Minimal setup allows you to run multiple variants against selected inputs with simple configuration, eliminating the manual effort of testing each variation individually. Visual comparison through the Evaluation UI provides side-by-side output analysis, making it easy to spot differences in quality, tone, accuracy, or completeness across different versions.

Version tracking maintains a complete history of code and prompt changes, enabling you to understand which modifications led to improvements and which caused regressions. Expert integration allows you to share evaluation results with domain experts when technical assessment isn't sufficient to determine the best approach.

Historical record keeping tracks which changes led to improvements over time, building institutional knowledge about what works well for your specific use case and domain. This systematic approach transforms prompt iteration from a manual, error-prone process into a reliable, data-driven workflow that builds confidence in your improvements.

Phase 1 Summary

By the end of Phase 1, you will have established a robust foundation for GenAI application development with comprehensive infrastructure, active data collection, systematic quality improvement processes, and readiness for scaling to broader deployment.

Infrastructure Established

Your technical foundation includes MLflow Tracing implemented for complete observability across all application components, feedback collection systems that capture input from both users and developers, expert review workflows that provide authoritative validation without technical barriers, and an evaluation harness for systematic testing of improvements.

Data Collection Active

Your systematic data collection captures real user queries from beta testing that reflect natural usage patterns, quality feedback that's systematically collected and organized for analysis, performance metrics tracked across different scenarios to understand application behavior, and expert assessments integrated directly with trace data for comprehensive quality evaluation.

Quality Improvement Process

The systematic approach to quality improvement includes systematic testing of prompt and code variations against realistic inputs, data-driven decisions based on real user interactions rather than assumptions, expert validation of improvements before broader deployment, and version tracking for confident promotion of successful changes to production.

Ready for Scale

Your Phase 1 implementation creates the foundation for production deployment with comprehensive monitoring capabilities, automated quality assessment using production data patterns, continuous improvement cycles based on systematic evaluation rather than ad-hoc changes, and scalable feedback collection from larger user bases without overwhelming manual processes.

Next Steps

With Phase 1 complete, you're ready to move to Phase 2: Systematically Testing Quality, Cost, and Latency where you'll scale your application to serve real users with confidence through systematic testing and performance optimization.

Phase 1 establishes the systematic development practices that will serve you throughout your GenAI application's lifecycle, ensuring reliable quality improvement and confident scaling.