GenAI Feedback & Retraining Framework: Architecture, Stack & Delivery
Designed a continuous improvement loop to capture feedback and retrain prompts/models for accuracy. Built with GenAI, Python, MLOps patterns.
By Yogendra Raghuvanshi
Introduction
In this article I break down how I designed and delivered GenAI Feedback & Retraining Framework — from the original business pain point through architecture, technology choices, implementation phases, and lessons learned. This is the same project featured in my portfolio's Built Solutions section, documented here in full technical depth for engineers, architects, and hiring managers who want to understand how the work was actually done.
I led this initiative as part of my broader program delivery work across enterprise AI, data platforms, and analytics transformation. The approach reflects how I operate: start with the business outcome, choose the minimum viable architecture, instrument everything, and iterate with real users.
Business problem
SQL generation quality drifted without systematic user feedback incorporation.
Designed a continuous improvement loop to capture feedback and retrain prompts/models for accuracy.
Architecture decisions
Key design choices that shaped reliability, performance, and maintainability of the solution.
- Separates prompt versions from model versions for traceability
- Human approval required before production prompt promotion
- Accuracy KPI shared with program stakeholders
Technology stack in depth
This project was built with GenAI, Python, MLOps patterns. Each technology was selected for a specific role in the architecture — not because it was trendy, but because it solved a measured bottleneck.
- GenAI: production component with documented integration patterns and operational runbooks
- Python: production component with documented integration patterns and operational runbooks
- MLOps patterns: production component with documented integration patterns and operational runbooks
Implementation timeline
Delivery followed phased milestones with explicit deliverables at each gate. This kept stakeholders aligned and made progress auditable for program reviews.
- Feedback capture UX (1 week): Thumbs up/down and correction capture on every agent response.
- → Feedback API
- → Analyst UI hooks
- → PII-safe logging
- Evaluation harness (2 weeks): Golden set of questions with expected SQL and scoring metrics.
- → Benchmark suite
- → Accuracy dashboard
- → Regression alerts
- Retraining loop (2 weeks): Prompt versioning and weekly retrain cadence with approval gate.
- → Prompt registry
- → Champion/challenger flow
- → Release notes
Feedback capture architecture
Every agent response exposes thumbs up/down and a free-text correction field. Events are written to an append-only store with session ID, question hash, generated SQL, correction SQL, and analyst ID. PII is stripped before persistence.
Corrections are not applied immediately — they enter a review queue where senior analysts approve entries for the golden evaluation set.
- Event schema: timestamp, prompt_version, model_version, latency_ms, outcome
- Weekly export to evaluation harness for accuracy scoring
- Champion/challenger prompt comparison before production promotion
Evaluation and release process
We treat prompts as versioned artifacts with the same rigor as application code. A benchmark suite of 200+ business questions runs on every candidate prompt. Regression alerts fire when accuracy drops more than 2% on any segment.
- Metrics: exact-match SQL, execution success rate, row-count sanity checks
- Human approval gate for prompt promotion to production
- Release notes shared with program stakeholders and data team leads
Business outcomes
Improved SQL generation accuracy and response quality over time.
Success was measured against adoption, latency/throughput targets, and stakeholder feedback — not just deployment dates. Program reviews tracked these KPIs alongside technical milestones.
Lessons learned
Treat GenAI like a product-measure quality and iterate with real users.
If I were starting again, I would invest even earlier in observability and golden test sets. The cost of retrofitting guardrails after pilot launch always exceeds building them in from day one.