VDF Model Evaluation Suite turns domain knowledge into repeatable test scenarios, runs them automatically across your entire model portfolio, and scores every response with quantitative metrics your risk and engineering teams can defend. No cloud uploads. No one-off chat sessions. A systematic baseline for every deployment decision.
Enterprise AI fails quietly — a fine-tuned model regresses after a prompt change, a cheaper model drifts on regulated language, or a new version passes a demo but breaks on edge cases your team already documented. Ad-hoc testing cannot catch this at scale. You need structured scenarios, consistent scoring, and persistent history tied to specific model versions.
A quantified accuracy profile for every candidate model — compared side-by-side against baselines, prior versions, and your own VDF AI agents — with exportable results that support pre-deployment sign-off, fine-tuning validation, and ongoing regression checks inside VDF AI Networks.
Demos prove a model can answer one question. Evaluation proves it can answer yours — consistently, across every model you might deploy.
Built into the VDF platform alongside VDF Data Suite and LLMFolio — one evaluation workflow for every model you operate.
Define evaluation scenarios with name, description, prompt, context details, expected answer, and reviewer notes. Build libraries aligned to your workflows — claims adjudication, contract clauses, policy Q&A, ticket triage — not generic public benchmarks that miss your terminology.
Tag models in LLMFolio for benchmarking, then run every use case against your full portfolio in one operation — local Ollama deployments, cloud models via OpenRouter, and VDF AI agent endpoints. One click replaces hours of per-model manual testing.
Every model response is scored with BLEU, ROUGE-L, METEOR, and BERTScore — covering n-gram overlap, recall, synonym awareness, and semantic similarity. Scores are stored per result so you can compare models, use cases, and runs over time.
Aggregated bar charts show metric scores across models at a glance. Drill into individual responses, filter by model or use case, and inspect the full output text alongside its scores — so engineers and reviewers see exactly where a model succeeded or failed.
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 they reach production — not after users report them.
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 — turning evaluation from a nice-to-have into an enforceable release control.
A repeatable four-step cycle — from scenario design to deployment decision.
Domain experts create test scenarios with prompts, context, and reference answers that reflect real production workloads — including edge cases that generic benchmarks ignore.
Execute all use cases against every benchmark-tagged model in LLMFolio. The platform handles provider routing, retries, and response capture — local and cloud models through a single workflow.
Automated evaluation computes BLEU, ROUGE-L, METEOR, and BERTScore for each response. Aggregated charts and per-result breakdowns reveal which model performs best on which scenario.
Promote models that meet your thresholds into VDF AI Networks. Archive results for audit review, re-run after changes, and maintain a living accuracy baseline across your model portfolio.
Lexical overlap alone misses paraphrased correct answers. Semantic similarity alone misses formatting requirements. VDF uses both.
A model can score high on BLEU by copying reference phrasing while being factually wrong on details METEOR and BERTScore would catch. Conversely, a semantically correct paraphrase may score low on BLEU but high on BERTScore. Running all four metrics on every response gives reviewers a multi-dimensional view — not a single number that hides failure modes.
Each evaluation run stores scores per model result with timestamps, enabling trend analysis across fine-tuning iterations, prompt template changes, and routing policy updates. That history is what turns a one-time test into an ongoing governance practice.
After training a domain model with VDF Data Suite, run the evaluation suite against your holdout scenarios before any production routing. Confirm the fine-tuned model actually outperforms the base model on tasks that matter — with numbers, not intuition.
Compare local open-weight models against cloud alternatives on identical domain prompts. Identify which model delivers the best accuracy-to-cost ratio for each workload before committing to a routing strategy in VDF AI Networks.
When system prompts, RAG templates, or agent instructions change, re-run the full use-case library to verify accuracy did not degrade. Catch prompt regressions in staging — not in production tickets.
Produce timestamped evaluation records tied to specific model versions, prompts, and reference answers. Support EU AI Act high-risk system documentation, internal model risk frameworks, and audit requests with evidence that goes beyond "we tested it manually."
Common questions about LLM evaluation with VDF AI.
Define your domain scenarios, benchmark every model on your infrastructure, and deploy with a quantified accuracy baseline your team can stand behind.