All news with #model evaluation tag

🔍 Evaluation engineering underpins reliable agentic systems and measuring behavior is harder than building the agent. Drawing on work developing the Google Cloud Developer plugin, the article presents four lessons: grade raw execution trajectories not final text, preserve turn structure in telemetry to catch parallel dispatch issues, run live and plan-only evaluations side-by-side, and treat evaluations as continuous integration against golden sets. The piece warns against one-shot demos and advocates stress-tested eval suites for safe deployment.

🛠️ This deep dive shows how to build a production-ready agent using the Agents CLI and Gemini Enterprise. It walks developers through six stages—Setup, Build, Deploy, Govern, Evaluate, and Publish—using an Industry Watch agent that reconciles press coverage with SEC filings. The tutorial emphasizes deterministic tools, managed runtime, memory, identity controls, and automated evaluations to prevent hallucination and ensure grounded, auditable results.

🧭 This essay, coauthored with Barath Raghavan and first published in The Guardian, argues for a new metric—the Genie coefficient—to measure how closely an AI’s actions match a user’s intended meaning. It explains why ordinary benchmarks miss the gap between literal compliance and reasonable, context-aware interpretation, and shows how modern harnesses can turn language models into proactive agents that take surprising, harmful shortcuts. The article outlines how Genie benchmarks should be designed, scored, and used to inform policy and harness constraints.

🧭 ASSERT is an open-source framework that converts natural-language behavior specifications into executable evaluation pipelines, generating test scenarios, datasets, metrics, and scorecards for models, agents, or applications. The pipeline systematizes intent into a concept spec, produces an editable behavior taxonomy, generates stratified test cases, records full inference traces, and scores each trace with rationales and policy citations. Internal validation showed ASSERT improves coverage, surfaces distinct failure patterns, and yields judge agreement with humans in most cases, while SME review confirmed alignment and credibility.

🧩Trustpilot built a high-volume streaming pipeline using fine-tuned Gemma models to process millions of user reviews in near real-time under tight latency and cost constraints. The team replaced variable per-token pricing with fixed infrastructure costs, fine-tuned lightweight models for tasks like NER, sentiment, and topic classification, and separated classifier and LLM endpoints. Performance tuning, vLLM optimizations, and load testing enabled scalable inference despite challenges with private networking, deployment observability, and GPU availability.

🚀 Google AI Edge Portal now enables developers to benchmark and debug on-device LLMs across a physical lab of over 120 representative Android devices. It profiles initialization time, prefill and decode speeds, and peak memory usage across CPU, GPU, and NPU backends to surface real user-impacting metrics. The integrated Model Explorer visualizes model graphs, tensor shapes, and traces to speed root-cause analysis and collaboration.

⚠️ AI hallucinations—confident but incorrect outputs—are increasingly driving risky decisions in critical infrastructure and cybersecurity operations, exploiting human trust in authoritative-sounding responses. A 2025 AA-Omniscience benchmark of 40 models found most systems were more likely to offer a confident wrong answer on difficult questions, underscoring that AI outputs must be treated as potential vulnerabilities until vetted. Effective controls include enforced human review before sensitive actions, treating training data as a security asset, strict least-privilege for AI systems, and prompt-engineering training to reduce ambiguous inputs.

🚀 Google Cloud presents its eighth-generation TPUs as two specialized systems: TPU 8t for massive pre-training and embedding workloads, and TPU 8i for low-latency sampling, serving, and reasoning. TPU 8t emphasizes throughput with a SparseCore for embedding collectives, native FP4 precision, VPU/MXU overlap, and the scale-out Virgo network to reduce DCN bottlenecks. TPU 8i prioritizes on-chip SRAM, a Collectives Acceleration Engine (CAE), and the Boardfly topology to cut network diameter and tail latency. The release is paired with a performance-first AI stack — Pallas, Mosaic, native PyTorch preview, and compatibility with JAX and Pathways.

🎯 Amazon Bedrock AgentCore Evaluations is now generally available to deliver automated, continuous and on-demand quality assessment for AI agents. The feature provides online evaluation to sample and score live production traces and on-demand evaluation for programmatic tests in CI/CD pipelines and interactive workflows. It includes 13 built-in evaluators covering response quality, safety, task completion and tool usage, plus Ground Truth and customizable LLM- or code-based evaluators.

🔍 This article outlines a practical, metrics-driven approach to testing generative AI systems, moving teams from ad-hoc inspection to systematic evaluation. It introduces four hands-on labs that cover evaluating single LLM outputs, assessing RAG systems with Vertex AI Evaluation, tracing and grading agent behavior with the Agent Development Kit (ADK), and validating SQL-generating agents against BigQuery. Each lab emphasizes measurable metrics—safety, groundedness, faithfulness, and factual accuracy—to help productionize GenAI with confidence.

🧭 Hugo Selbie presents a practical framework for evaluating modern multi-step AI agents, emphasizing that final-output metrics alone miss silent failures arising from incorrect reasoning or tool use. He recommends defining clear, measurable success criteria up front and assessing agents across three pillars: end-to-end quality, process/trajectory analysis, and trust & safety. The piece outlines mixed evaluation methods—human review, LLM-as-a-judge, programmatic checks, and adversarial testing—and prescribes operationalizing these checks in CI/CD with production monitoring and feedback loops.

🔐 CrowdStrike explains how inadvertent 'leakage' — when dependent or correlated observations are included in training — can inflate machine learning performance and undermine threat detection. The article shows that blocked or grouped data splits and blocked cross-validation produce more realistic performance estimates than random splits. It also highlights trade-offs, such as reduced predictor-space coverage and potential underfitting, and recommends careful partitioning and continuous evaluation to improve cybersecurity ML outcomes.