THE PROBLEM
Enterprise AI Fails Quietly
A fine-tuned model regresses. A cheaper model drifts on regulated language. A new version passes a demo but breaks on documented edge cases. You need more than chat sessions to catch this.
Unrepeatable Tests
Ad-hoc chat sessions produce different results every time. No structured reference answers, no version control, no audit trail. Last week's "it works" is this week's unknown.
Silent Regressions
A prompt change, model update, or routing policy shift can degrade accuracy without any alert. You discover the problem when users report wrong answers — not before deployment.
No Audit Evidence
Regulators and risk committees want timestamped evaluation records tied to specific model versions. "We tested it in a chat window" is not evidence — and the EU AI Act agrees.
Systematic evaluation is the only way to know what a model will do before it does it in production.
HOW IT WORKS
Four Steps. One Baseline.
A repeatable cycle — from scenario design to deployment decision — that runs entirely on your infrastructure.
Define Use Cases
Domain experts create test scenarios with prompts, context, and reference answers — including edge cases generic benchmarks ignore.
Run Batch Benchmarks
Execute every use case against your full model portfolio — local Ollama, cloud via OpenRouter, and VDF agents — in a single operation.
Score & Compare
Automated evaluation computes BLEU, ROUGE-L, METEOR, and BERTScore for every response. Side-by-side charts reveal which model performs best on which scenario.
Decide & Deploy
Promote models that meet your thresholds into VDF AI Networks. Archive results for audit. Re-run after changes.
METRICS
Four Metrics, One Complete Picture
Lexical overlap alone misses paraphrased correct answers. Semantic similarity alone misses formatting requirements. VDF uses both.
BLEU Score
PrecisionMeasures n-gram precision between model output and reference text. Computed across unigram through 4-gram weights for a balanced surface-level alignment view.
ROUGE-L
RecallEvaluates longest common subsequence overlap. Catches when key phrases from your reference answer must appear in the response, regardless of surrounding text.
METEOR
Synonym-AwareGoes beyond exact word matches by considering synonyms, stemming, and word order. Catches correct answers in different phrasing that BLEU would penalize.
BERTScore
SemanticUses transformer embeddings to compare semantic similarity. Detects when a response means the right thing but uses entirely different vocabulary.
Why all four together: A model can score high on BLEU by copying reference phrasing while being factually wrong on details METEOR and BERTScore would catch. Running all four on every response gives reviewers a multi-dimensional view — not a single number that hides failure modes.
CAPABILITIES
From Test Scenario to Deployment Decision
Built into the VDF platform alongside VDF Data Suite and LLMFolio — one evaluation workflow for every model you operate.
Domain Use Case Libraries
Define evaluation scenarios with name, prompt, context, expected answer, and reviewer notes. Build libraries aligned to your workflows — claims adjudication, contract clauses, policy Q&A — not generic public benchmarks.
Multi-Model Batch Benchmarking
Tag models in LLMFolio, then run every use case against your full portfolio in one operation — local Ollama, cloud via OpenRouter, and VDF agent endpoints. One click replaces hours of manual testing.
Visual Comparison & Filtering
Aggregated bar charts show metric scores across models at a glance. Drill into individual responses, filter by model or use case, and inspect full output text alongside scores.
Regression Detection
Re-run the same use-case library after a model update, fine-tuning iteration, or prompt change. Compare new scores against prior baselines to catch accuracy drops before production.
Pre-Deployment Evaluation Gates
Integrates with the VDF fine-tuning lifecycle as a mandatory validation step. Candidate models must meet your accuracy thresholds before promotion into VDF AI Networks.
Exportable Audit Trail
Timestamped evaluation records tied to specific model versions, prompts, and reference answers. Support EU AI Act high-risk system documentation and internal model risk frameworks.
COMPARISON
Chat Windows Test. VDF Evaluates.
| Capability | VDF Evaluation Suite | Manual Chat Testing | Open-Source Eval Frameworks |
|---|---|---|---|
| Structured domain-specific test scenarios | ✓ built-in libraries | ✗ ad hoc prompts | △ requires coding |
| Multi-model batch benchmarking | ✓ one-click full portfolio | ✗ one model at a time | △ scripted per model |
| 4 complementary metrics (BLEU, ROUGE, METEOR, BERT) | ✓ automatic on every run | ✗ subjective review | △ manual integration |
| Regression detection across versions | ✓ baseline comparison | ✗ | △ build your own |
| Fully on-premise / air-gap | ✓ by design | ✗ cloud APIs | ✓ self-hosted |
| Audit-ready timestamped records | ✓ per model & version | ✗ | ✗ |
| Pre-deployment evaluation gates | ✓ integrated lifecycle | ✗ | ✗ |
Categories shown for orientation; detailed feature comparisons available on request.
USE CASES
Where Evaluation Prevents Costly Mistakes
Fine-Tuning Validation
After training a domain model with VDF Data Suite, run the evaluation suite against holdout scenarios before production routing. Confirm the fine-tuned model actually outperforms the base model — with numbers, not intuition.
Model Selection & Vendor Comparison
Compare local open-weight models against cloud alternatives on identical domain prompts. Identify which model delivers the best accuracy-to-cost ratio before committing to a routing strategy in VDF AI Networks.
Prompt & Template Regression
When system prompts, RAG templates, or agent instructions change, re-run the full use-case library to verify accuracy did not degrade. Catch regressions in staging — not in production tickets.
Compliance & Model Risk Documentation
Produce timestamped evaluation records tied to specific model versions, prompts, and reference answers. Support EU AI Act high-risk system documentation and audit requests with evidence that goes beyond "we tested it manually."
PLATFORM
Where Evaluation Fits in the Stack
Evaluation is the gate between training and production — the step that turns model selection from opinion into evidence.
1 · Data Suite
Curate training data, build RAG pipelines, and fine-tune domain models.
2 · Evaluation
Benchmark candidates with domain scenarios and quantitative metrics.
3 · AI Router
Route requests to the best model by quality, cost, latency, and energy.
4 · AI Networks
Orchestrate multi-agent workflows with audit trails and governance.
Continuous loop: re-run evaluation after every model update, prompt change, or routing policy adjustment. The same use-case library is your living baseline — not a one-time sign-off.
FAQ
Model Evaluation Questions
Go Deeper
Understand the platform context before choosing an evaluation strategy.
Stop Guessing. Start Measuring.
Define your domain scenarios, benchmark every model on your infrastructure, and deploy with a quantified accuracy baseline your team can stand behind.