You will get AI Regression Test Suite - Golden Dataset + LLM-as-Judge + CI Integration
Project details
Your prompt breaks silently. Users complain. You scramble to debug.
This stops that. Automated tests catch quality drops before they reach production.
The problem: You update a prompt, change a model, or tweak a parameter. Everything looks fine in manual testing. But next week, users start complaining that answers are worse. You have no baseline, no way to know what changed.
Real example: In my multi-agent platform, golden dataset caught 3 prompt regressions before they shipped to users.
What I build:
• Golden Dataset — versioned test cases with required keywords, forbidden phrases, min quality scores. Append-only, tied to prompt versions.
• LLM-as-Judge — separate model scores outputs on relevance, factuality, format (0.0–1.0 each).
Three test layers: Smoke (alive?), Functional (correct structure?), Regression (quality vs baseline).
• CI integration — tests run on every push. Deploy blocked if quality drops.
What you get:
• Working test suite in your repo
• Baseline scores for comparison
• GitHub Actions pipeline configured
• Documentation
This stops that. Automated tests catch quality drops before they reach production.
The problem: You update a prompt, change a model, or tweak a parameter. Everything looks fine in manual testing. But next week, users start complaining that answers are worse. You have no baseline, no way to know what changed.
Real example: In my multi-agent platform, golden dataset caught 3 prompt regressions before they shipped to users.
What I build:
• Golden Dataset — versioned test cases with required keywords, forbidden phrases, min quality scores. Append-only, tied to prompt versions.
• LLM-as-Judge — separate model scores outputs on relevance, factuality, format (0.0–1.0 each).
Three test layers: Smoke (alive?), Functional (correct structure?), Regression (quality vs baseline).
• CI integration — tests run on every push. Deploy blocked if quality drops.
What you get:
• Working test suite in your repo
• Baseline scores for comparison
• GitHub Actions pipeline configured
• Documentation
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Content Creation, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$100
|
Standard
$600
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 10 days |
Number of Revisions | 2 | 2 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | - | |
Model Monitoring | - | ||
Model Testing & Optimization | |||
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$400 - $900
Additional Revision
+$50Frequently asked questions
About Slava
AI Systems Engineer | LLM Agents - RAG - NestJS - LangGraph
Kyiv, Ukraine - 10:39 pm local time
Most AI developers will get it working. I focus on keeping it working.
What that looks like in practice:
Multi-agent pipelines, RAG over 50k+ documents, NestJS backends
with circuit breakers and proper fallback chains. At EU DisinfoLab
I built all of this from scratch — not as a side project, as the
actual production system.
LangGraph pipelines where agents actually know when to stop and why.
RAG that returns the right documents, not just the closest ones —
brand-isolated Qdrant collections, score gating, episodic memory.
NestJS backends with per-provider rate limiting, 429-aware retry,
circuit breakers, SSE streaming under 200ms first token.
AI eval tests that catch prompt degradation before it hits production.
---
Current work: EU DisinfoLab (since Oct 2024) — built the full AI layer,
LangGraph pipeline processing 10k+ items, RAG over 50k docs,
Kubernetes from scratch with GitOps and zero-downtime deploys.
Stack: TypeScript · NestJS · LangGraph · Zod · Qdrant · Redis · BullMQ
Kubernetes · OpenTelemetry · OpenAI · Claude · Gemini · Vertex AI
English: strong written, async-first communication.
Steps for completing your project
After purchasing the project, send requirements so Slava can start the project.
Delivery time starts when Slava receives requirements from you.
Slava works on your project following the steps below.
Revisions may occur after the delivery date.
Audit agent & define test scenarios
Review your agent's behavior, identify critical paths and edge cases
Build golden dataset
Create 10-20 versioned test cases with expected outputs, required keywords, forbidden phrases