Engineering Guide for AI/ML and MLOps Engineers
Engineering Guide for AI/ML and MLOps Engineers
5 Practical Lessons on Evaluating Production LLM Systems
5 Practical Lessons on Evaluating Production LLM Systems
5 Practical Lessons on Evaluating Production LLM Systems
Learn how to build evals that catch subtle failures in LLM systems over structured data — from domain-aware metrics to automated pipelines.
Learn how to build evals that catch subtle failures in LLM systems over structured data — from domain-aware metrics to automated pipelines.
Based on SPD Technology’s AI/ML engineering experience
Based on SPD Technology’s AI/ML engineering experience
Structured data workflows
Domain-aware metrics
Component-level evals
Automated eval pipelines
What’s inside the guide
What’s inside the guide
5 engineering lessons on evaluating production LLM systems over structured data.
5 engineering lessons on evaluating production LLM systems over structured data.
01
Architecture changes.
Evals should not
Build evaluation datasets that survive prompt, model, and system rewrites.
02
Generic metrics miss domain failures
Use domain knowledge to catch wrong operators, taxonomy mistakes, and high-impact errors.
03
End-to-end scores hide broken components
Evaluate extraction, tool selection, classification, safety layers, and responses separately.
04
Stable scores can create false confidence
Design sensitive evals with production-like distributions, per-field metrics, and calibrated judges.
05
Bugs are not one-off fixes
Turn production failures into dataset samples and automate regression checks.
Who it’s for:
Who it’s for:
For AI/ML, MLOps, and product engineers working with LLM systems in production.
For AI/ML, MLOps, and product engineers working with LLM systems in production.
01
Architecture changes. Evals should not
Build evaluation datasets that survive prompt, model, and system rewrites.
02
Generic metrics miss domain failures
Use domain knowledge to catch wrong operators, taxonomy mistakes, and high-impact errors.
03
End-to-end scores hide broken components
Evaluate extraction, tool selection, classification, safety layers, and responses separately.
04
Stable scores can create false confidence
Design sensitive evals with production-like distributions, per-field metrics, and calibrated judges.
05
Bugs are not one-off fixes
Turn production failures into dataset samples and automate regression checks.
Built from years of AI-powered product engineering
Built from years of AI-powered product engineering
Since 2007, SPD Technology has worked with PitchBook across high-trust data, product infrastructure, and AI/ML systems.
Since 2007, SPD Technology has worked with PitchBook across high-trust data, product infrastructure, and AI/ML systems.
SPD Technology
SPD Technology is a global software product development company that creates cutting-edge tech solutions that drive clients’ growth.
PitchBook
PitchBook is a platform for private market intelligence that combines proprietary data, research, and AI systems to support real investment decisions.
20+
years in the industry
650+
experts on board
72%
Senior+ engineers
20+
years in the industry
650+
experts on board
72%
Senior+ engineers
PitchBook
PitchBook is a platform for private market intelligence that combines proprietary data, research, and AI systems to support real investment decisions.
10M+
companies in datasets
100,000+
users globally
19
years on the market
Where engineering empowers product growth
Where engineering empowers product growth
Explore current AI/ML and MLOps roles at SPD Technology
Go deeper into production LLM evaluation
Go deeper into production LLM evaluation
Explore five practical lessons for catching subtle failures in LLM systems over structured data from durable eval datasets to automated pipelines