Guided walkthroughs with clear objectives and verifiable checkpoints. Learn how to build production RAG, multi-agent systems, evaluation suites, and automated governance.
First-time setup: attaching Scope to an LLM application.
Observing retrieval quality, vector search, and generation quality.
Tracing agent collaboration, tool selection, and reasoning paths.
Generating interpretable prompt and context propagation insights.
Enforcing security policies, RBAC, and automated release gating.
Driving observability, evaluation, and profiling entirely from terminal scripts.
Embedding NeuronScope into custom frameworks and internal platforms.
Building, testing, and packaging platform extensions.
Building fair, standardized benchmarks for LLM systems.
Measuring latency, token consumption, and GPU memory hotspots.
Analyzing decision trees and prompt influence across workflows.
Guided learning paths for building, observing, and governing AI systems.
Task-oriented recipes for AI engineering.
Runnable, self-contained AI engineering code samples.
Research-driven AI engineering and system interpretability.