Case studies: neural assistants in real tasks

Goal: Summarize multiple documents into key claims, risks, and open questions. Workflow: feed the assistant one source at a time, capture a structured digest, then merge. Quality: verify top claims in the originals, and remove anything without support.

• Output includes “What we know” vs “What we assume”
• Each claim mapped to a source excerpt location

Goal: Produce a first draft quickly while preserving your voice. Workflow: request an outline, then a draft, then a rewrite pass for tone and clarity. Quality: check facts, remove generic filler, and add your own examples.

• Three alternatives for the introduction
• Final pass includes a readability and bias check

Goal: Convert readings into practice questions and a revision schedule. Workflow: summarize chapter, extract terms, generate questions, then schedule practice sessions. Quality: compare answers to the textbook and refine weak topics.

• Questions tagged by topic and difficulty
• Weekly review list based on error rate

Goal: Break a project into tasks with dependencies, owners, and risks. Workflow: define scope, ask for a plan, then run a “risk brainstorm” prompt. Quality: validate estimates with your team and update the plan based on constraints.

• Plan includes assumptions and decision points
• Risks include mitigations and early warning signals