Results for "real-world testing"

A continuous vector encoding of an item (word, image, user) such that semantic similarity corresponds to geometric closeness.

Architecture that retrieves relevant documents (e.g., from a vector DB) and conditions generation on them to reduce hallucinations.

Automated detection/prevention of disallowed outputs (toxicity, self-harm, illegal instruction, etc.).

Constraining model outputs into a schema used to call external APIs/tools safely and deterministically.

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.

Attention mechanisms that reduce quadratic complexity.

Methods for breaking goals into steps; can be classical (A*, STRIPS) or LLM-driven with tool calls.

Separates planning from execution in agent architectures.

Models trained to decide when to call tools.

Recovering 3D structure from images.

Detects trigger phrases in audio streams.

Model execution path in production.

Optimal estimator for linear dynamic systems.

Centralized repository for curated features.

Running predictions on large datasets periodically.

Interleaving reasoning and tool use.

Using production outcomes to improve models.

Cost to run models in production.

Variable whose values depend on chance.

Maximizing reward without fulfilling real goal.

Dynamic resource allocation.

Running models locally.

Internal sensing of joint positions, velocities, and forces.

External sensing of surroundings (vision, audio, lidar).

Continuous loop adjusting actions based on state feedback.

Using output to adjust future inputs.

Planning via artificial force fields.

Ensuring robots do not harm humans.

Closed loop linking sensing and acting.

AI systems assisting clinicians with diagnosis or treatment decisions.