Benchmark Your Way to Better RAG and Agents:Tuning Vector Search with MLflow
Summary
Tuning vector search for RAG and AI agents requires isolating one configuration knob at a time against a fixed evaluation set rather than relying on default settings. Using MLflow retriever evaluation to track metrics like precision@k and nDCG@k lets teams systematically measure and compare the impact of embedding models, chunk sizes, and rerankers side by side.
Summary generated by brickster.ai. For the full article, follow the source link above.
More from MLflow Blog
Can Jev replace your LLM judge? Part 2: Testing harder answers
TypeSafe's Jev model evaluates MLflow QA answers roughly seven times faster and significantly cheaper than larger LLMs, but it misses subtly incorrect technical details such as reversed roles and omitted permissions. Ahead of Jev integration arriving in MLflow 3.17, practitioners can use its confidence probabilities to fast-track clear verdicts while escalating borderline cases to a larger model for review.
Can Jev replace your LLM judge? Evaluating quality, cost, and latency
Benchmarking Jev against GPT, Claude, and DeepSeek using MLflow reveals how it measures up as an alternative LLM judge. Review side-by-side performance across quality, cost, and latency to see if Jev can replace your current evaluation models.
Evaluating and Improving Agent Skills with MLflow
MLflow enables evaluation-driven development for AI agent skills by combining traces, evaluation datasets, custom evaluators, and experiment tracking to treat agent capabilities like testable software components. Instead of grading only final outputs, teams can use execution traces to systematically measure critical behaviors such as tool sequencing, policy compliance, and execution efficiency across skill iterations.
Review Queues: The Human Step Towards Better AI
MLflow Review Queues turn AI trace review into a ticketing system equipped with assignments, status tracking, and human evaluations. Teams can now move away from tracking AI degradations and misbehavior in spreadsheets.
Multi-Harness AI Agents Need Multi-Layer Observability: Omnigent in MLflow
Omnigent unifies multi-harness agent orchestration and now delivers automatic observability across every agent with MLflow Tracing, requiring no code changes. This post details how Omnigent in MLflow provides multi-layer observability for multi-harness AI agents.
How to Manage your LLM Teams using MLflow's Role-Based Access Control
MLflow's new Role-Based Access Control (RBAC) helps LLM teams define reusable roles, isolate workspaces, and enforce fine-grained permissions across prompts, experiments, and AI Gateway resources. Learn how to manage your LLM teams using these new MLflow RBAC capabilities.