Practical recipes with copy-pasteable Python SDK and CLI code for observing RAG applications, evaluating hallucination scores, profiling GPU memory, and gating CI/CD pipelines.
Compute deterministic identity for streaming LLM training/eval data.
Row-order-independent hashing for vector databases and embeddings.
Evaluate quality, accuracy, and latency deltas between two models.
Reproducible diff across staging and production environments.
Render telemetry artifacts into browsable HTML engineering reports.
Chaining ns subcommands in Bash and PowerShell for CI pipelines.
Regression tests against pinned reference evaluation benchmarks.
Building custom evaluation metrics and vector store adapters.
Reading and writing .nsz artifacts to S3, GCS, and Azure Blob.
Memory-bounded profiling across high-throughput inference runs.
True streaming observability with deterministic replay.
Executing multi-model benchmarks in parallel.
Runnable, self-contained AI engineering code samples.
Step-by-step walkthroughs for all experience levels.
Concepts, semantics, and contracts for every platform capability.