30-Minute Quick Course: Mastering AI Engineering — Learn

Welcome to the NeuronScope 30-Minute Quick Course! This course equips software developers and AI engineers with the complete lifecycle skills needed to observe, evaluate, explain, profile, and govern modern AI systems.

1. Install Platform Core

NeuronScope installs via pip or uv. The base package includes the AI Kernel, CLI, and PyTorch / JAX / LLM API adapters.

terminal

bash

pip install "neuronscope[all]==0.9.2"
ns doctor

2. Observe AI Systems

Attach non-invasive observability scopes to LLMs, SLMs, RAG retrieval pipelines, and autonomous agents.

observe_system.py

python

import neuronscope as ns

# Attach non-invasive scope to application
with ns.Scope("rag_service", monitor_all=True) as scope:
    result = rag_pipeline.query("Explain latency bottleneck")

print(f"Captured Telemetry Digest: {scope.fingerprint}")
scope.save("baseline_run.nsz")

3. Understand & Evaluate Quality

Run standardized evaluators to compute factual accuracy, hallucination rates, and retrieval relevance.

evaluate_run.py

python

from neuronscope import Evaluator, load_artifact

artifact = load_artifact("baseline_run.nsz")
report = Evaluator.run_suite(artifact, evaluators=[
    Evaluator.HallucinationScore,
    Evaluator.RetrievalRelevance
])

print(f"Quality Score: {report.quality_score}% | Status: {report.status}")

4. Explain & Profile Latency

Break down execution latency across prompt tokenization, vector store lookup, LLM token generation, and tool execution.

terminal

bash

ns inspect baseline_run.nsz --profile

5. Optimize & Govern Production

Enforce deployment guardrails, PII redaction rules, and CI regression gating.

govern_ci.py

python

import neuronscope as ns

baseline = ns.load_artifact("baseline_run.nsz")
candidate = ns.load_artifact("candidate_run.nsz")

diff = ns.compare(baseline, candidate)
if diff.has_regression(threshold=0.02):
    raise SystemExit("CI Gating Failed: Quality regression detected in candidate deployment!")

6. Next Learning Paths

Activation Basics — deep dive on intermediate tensor observation.
Comparing Production Runs — regression diffing techniques.
Platform Architecture — the 8-layer AI engineering model.