Test whether irrelevant context changes alter an AI decision. Preserve task, evidence, and policy while distinguishing genuine behavioral differences from changed settings or grading defects.
Understand how neural networks learn relationships, then separate model training from retrieval, context, and application memory. Use those distinctions to make clearer enterprise AI architecture decisions.
Decide which agent observations may become durable memory, procedures, or training data. Build qualification, evaluation, release, and revocation paths that preserve evidence without automatically approving the lesson.
Use the double-slit experiment as a bounded analogy for AI context and evaluation. Distinguish presentation effects from changed evidence, and turn the comparison into a controlled testing approach.
Choose the right mechanism for AI context, retrieval, persistent records, and workflow state. Design provenance, permitted use, correction, and revocation across both the memory write and read paths.
Make RAG citations useful evidence rather than decorative references. Validate source identity, claim support, applicability, and disclosure permission while keeping citation destinations under application control.
Carry the effective user’s permissions through the entire RAG workflow. Enforce access before generation, preserve restrictions through caches and history, and test behavior when permissions change.
Treat ingestion, chunking, embedding, and storage as governed data processing. Preserve source restrictions and lineage, control processing routes, and design revocation and recovery before expanding the RAG corpus.
Retire an AI service across its endpoints, identities, data, delegated work, and recovery automation. Verify that it cannot keep acting or silently return through an overlooked deployment path.
Recover the retrieval service, not just vector database files. Coordinate metadata, lineage, embeddings, permissions, and configuration, then validate authorized retrieval before reopening production traffic.
Design AI inference recovery around the complete approved service. Include models, retrieval, authorization, application state, capacity, interrupted requests, and tested degraded modes.
Turn context-evaluation findings into production controls. Preserve critical evidence through retrieval and summarization, assign ownership, and keep release evidence separate from runtime execution authority.
Identify what actually changes when an AI system behaves differently. Separate model parameters, instructions, retrieval, memory, tools, and feedback, then govern each layer according to its persistence and authority.
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