Everything you need to know about evals
Quick guides, implementation tips, and practical insights for building production-ready AI evaluation systems.
What "evals" actually are
TL;DR: Evals are production quality gates that catch AI failures before they break user experience.
Connect real conversations to automated quality checks
Turn failure patterns into measurable criteria
Gate deploys with confidence, fix what actually matters
Quick TLDR Tips
Start with Failure Patterns
Read 40-100 real traces. Write one note per failure. Group them into 4-7 actionable categories.
Code First, Judge When Needed
Use binary checks for output format, tool calls, policy gates. Only use LLM-as-judge for semantic decisions.
Gate From Day 0
Set meaningful thresholds: Faithfulness ≥80%, Answer Relevancy ≥75%, Tool Correctness ≥95%.
Instrument Everything
Log traces, scores, and metadata. Use Langfuse or similar for observability. Alert on threshold violations.
Ready to dive deeper into AI evaluation strategies?
Production-Grade AI Evaluation
Complete framework for LLM evaluation in production.
Metrics • Automation • Governance • Improvement
Metrics
Define and implement core evaluation metrics
QAG, G-Eval, Contextual P/R implementation with ≥80% metric coverage
Automation
Automate evaluation in CI/CD pipelines
Zero manual evaluation with <5min gate time in your deployment pipeline
Governance
Establish ownership and review processes
Clear role definitions with quarterly reviews and metric ownership
Improvement
Full-funnel logging and tracing for complete observability
Threshold tuning and dataset refresh for improving quality scores
Why M.A.G.I. Works
Production-Ready
Battle-tested by leading AI companies
Fully Automated
Zero manual evaluation overhead
Continuously Improving
Data-driven refinement & optimization
Two essential evaluator types
Combine deterministic code checks with semantic LLM judges for comprehensive AI evaluation coverage.
Cheap checks you can write as code: JSON/markdown validity, required fields present, right tool called, confirmation language before transfer, latency/cost thresholds, etc. These don't need an LLM—just rules and string/structure checks.
Common Examples:
Key Benefits:
Narrow, binary judgments that require understanding: "Should this have been handed to a human?", "Is the answer grounded in the provided context?", "Did the reply misrepresent availability?" These use an LLM to return TRUE/FALSE for a single failure mode.
Common Examples:
Key Benefits:
Where popular tools fit
Understanding how RAGAS and Ranx map to these categories
RAG content quality
RAGAS sits squarely in the semantic/LLM-as-judge bucket (focused on RAG content quality). It provides metrics like:
Key Metrics:
Note: It's excellent for evaluating the RAG slice of your system, but it doesn't cover agent workflow/UX metrics (handoff-miss, promises kept, transfer confirmation), output-contract checks, safety/PII, latency, or business KPIs.
RAGAS: Think of RAGAS as your "content quality" module under the LLM-judge category; pair it with code checks and behavior judges to get full coverage.
Retrieval system quality
Ranx is a fast information-retrieval (IR) evaluation toolkit for ranking systems. You feed it qrels (relevance labels) and runs (your retrieval results), and it computes classic IR metrics such as:
Key Metrics:
Ranx: Clean separation: Retriever quality → Ranx
Recommended Practice
Modern metrics that actually matter
Move beyond legacy BLEU/ROUGE scores. Use semantic, grounded, and task-oriented metrics that correlate with real user satisfaction.
≤ 5 metrics per use case • Quantitative & human-aligned • Business outcomes first
From liability to reliability
See how proper evaluation frameworks catch dangerous edge cases before they reach production.
Problem
User asks about coffee machine reimbursement. Retriever finds permissive policy but misses exclusions list.
Impact
Partially correct but dangerous answer - "Yes, you can expense it"
Solution
QAG + Contextual Recall caught the missing exclusion document
Results
Problem
User receives answer based on outdated policy. Current policy exists but buried in different section.
Impact
40% of policy queries returned outdated information
Solution
Temporal metadata + filtering + evaluation gates
Results
Problem
Agent calls non-existent tools or uses malformed parameters in production
Impact
System crashes and failed user tasks
Solution
Tool Correctness metric with ≥95% threshold
Results
Every case study demonstrates the critical importance of proper evaluation gates