Insights on AI evaluation, experimentation, and building reliable AI products.
What a Forward Deployed Engineer actually does at ellamind: onboarding customers onto elluminate, carrying the friction they don't file back into the roadmap, and closing the loop between a customer's reality and the product.
A CLAUDE.md file's value isn't the facts it supplies. It's the workflow it imposes. We ran Claude Opus 4.6 on ten Django tasks with and without our project's instructions, and the cost gap came almost entirely from one workflow choice the file encodes.
When a German health care provider needed to replace the LLM powering its customer-facing AI platform, structured evaluation turned a risky model switch into a data-driven decision. Uncovering fabricated personal data, leaked system instructions, and costly wrong reimbursement decisions along the way.
How N+1 evaluation catches the failures that single-turn testing misses, and how to set it up in elluminate.
We re-ran OpenAI's FrontierScience benchmark on GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro using elluminate. Here's where the latest frontier models stand on the hardest public science benchmark.
How structured evaluation turned a capable but unreliable AI agent into one that processes PKV claims at 93% accuracy. Same model, same cases, different instructions.
Quality ownership drifts when AI systems fail quietly. Learn why plausible-sounding outputs are dangerous, what ownership actually means for AI teams, and how to scale evaluation rigor with risk.
Running Kubernetes in production on multiple cloud providers means juggling OpenTofu configurations, Helm charts, and deployment pipelines. Here's how we use Claude Code as an infrastructure copilot with safety guardrails, custom skills, and encoded domain knowledge.
We tested 168 sensitive China-related topics across 10 LLMs. One Chinese model matched GPT-5.2 and Claude. Another rewrote the Tiananmen massacre as state-approved fiction.
A complete framework for RAG evaluation covering test set design, targeted criteria for retrieval and generation, experiment analysis, and continuous production monitoring.
Your evaluations say your AI is perfect. You know it's not. Here's how we used MCP to iterate rapidly and surface real limitations.
Import your Langfuse datasets directly into elluminate. Turn production traces into structured evaluations - no export scripts or CSV wrangling required.
Binary pass/fail evaluations beat Likert scales for LLM and agent evaluation. Here's why, and how to keep nuance without the inconsistency.
How we built a test set for a German health insurer's AI search—from 50 real user queries to 80 cases, 57 experiments, and a pass rate that climbed from 35% to over 80%.
Learn how to systematically test and improve your AI prompts using elluminate's evaluation platform. Walk through a complete example using pizza toppings to understand prompt templates, collections, criteria, and experiments.
See how our products can help you evaluate, deploy, and monitor AI agents with confidence.