Practitioner library
Deep, decision-focused material for the assurance problems that do not fit a generic checklist.
22 resources in AI Assurance
AI Assurance
A field-ready method for deciding whether an AI-enabled GxP workflow is fit for release—without mistaking model accuracy, vendor claims, or human review for a complete assurance argument.
AI Assurance
An assistant produces a list of protocol findings in seconds. The reviewer finishes faster. That looks promising, but time alone does not establish whether the review improved.
Foundation · Video + deep read
Identify everything that can influence the AI-assisted outcome. A model is one component. A different document parser can drop a table; a retrieval filter can select an obsolete procedure; a reviewer can accept an unsupported answer; an integration can save it to the wrong record.
Foundation · Video + deep read
A predictive model estimates a value or category. A generative model produces content. A RAG application supplies retrieved material to support generation. An agent can select or execute actions through tools. These descriptions can overlap within one application.
Foundation · Video + deep read
“Helps Quality” is too broad. State what the assistant does, with which information, for which users, before which decision, and with which limits. Context includes the user’s expertise, workload, available sources, and consequences of an incorrect answer.
Foundation · Video + deep read
GxP is shorthand for several regulated good-practice areas. Determine the applicable process and obligations rather than treating GxP as a single global rulebook.
Practitioner · Video + deep read
Someone must own the use, technical service, quality decision, information protection, monitoring, and incident response. One person may hold several responsibilities, but gaps should not be hidden behind a general “AI team” label.
Practitioner · Video + deep read
“Hallucination risk” is too general to design an adequate test. Identify the unsupported statement, the use of that statement, and what could happen next.
Practitioner · Video + deep read
An evidence plan connects a claim to a test, review, analysis, or operational control. Avoid collecting only evidence that the application runs or that users like it.
Practitioner · Video + deep read
Data lineage connects a source to its transformations and use. For an AI reviewer, that may include document version, extraction, chunking, indexing, retrieval, generation, and human disposition.
Practitioner · Video + deep read
If developers repeatedly tune the prompt against the same cases and then report performance on those cases, the result may overstate performance on new work. Separate development material from evaluation material and manage access to reference answers.
Practitioner · Video + deep read
Review the supplied information about capabilities, limitations, version identification, changes, availability, security, data handling, and service terms. Then determine which questions remain about your application and use.
Practitioner · Video + deep read
For issue detection, define what counts as a distinct finding and how it matches a reference issue. Precision asks how many proposed findings are correct. Recall asks how many reference issues were found. Neither measure alone establishes safe or useful deployment.
Practitioner · Video + deep read
Include representative normal work and cases designed to expose known failures. A challenge set should cover the intended use and its limits, not just unusual puzzles.
Advanced · Video + deep read
Check source eligibility, extraction and indexing, retrieval relevance, answer support, and user interpretation. A failure at an early stage can make later answer evaluation misleading.
Advanced · Video + deep read
An assistant can invent a fact, omit a qualifying condition, overstate certainty, or combine true statements into an unsupported conclusion. Review meaning at the claim level.
Advanced · Video + deep read
The reviewer needs suitable competence, time, source access, authority, and a usable way to reject or escalate output. The interface should make the generated status clear and preserve the final human decision.
Advanced · Video + deep read
Separate transient generation from the approved outcome. Determine which inputs, versions, outputs, review decisions, and technical details are needed for traceability, investigation, retention, and applicable obligations. Do not assume every token must be retained or that nothing matters because the output began as a draft.
Advanced · Video + deep read
Assess changes to prompts, retrieval, documents, parsing, embedding models, permissions, tools, UI, review instructions, and downstream use. Identify which claims the change could affect.
Advanced · Video + deep read
Choose indicators tied to meaningful failure: confirmed missed issues, false findings, wrong sources, unauthorized retrieval, reviewer overrides, tool failures, and unsupported use. Assign owners, review frequencies, thresholds or event triggers, and actions appropriate to the process.
Advanced · Video + deep read
A document or message can contain text that attempts to redirect the assistant. The system should distinguish data used for the task from authority to change its task, disclose information, or execute tools.
Advanced · Video + deep read
A team evaluates an assistant that suggests protocol-review findings. It uses controlled requirements, generates source-linked candidates, and records reviewer dispositions. Approval and production release are outside its permissions.