A teammate is debugging a Claude application whose system prompt has grown to several hundred lines and now contains overlapping, contradictory, and obsolete instructions.
How would you advise the teammate?

• A. Tighten only the contradictory rules first, treating the overlap and obsolete content as lower-priority work the team can address later.
• B. Audit the prompt for overlap, contradiction, and obsolete content, then refactor so each instruction is clear, current, and non-redundant.
• C. Add more explicit instructions so the most recent rules dominate the model's interpretation of the prompt during each request.
• D. Split the prompt across multiple system prompts so the model sees a smaller portion at any given time.

You are reviewing an architectural diagram for a Claude-powered travel-booking system. The diagram shows a top-level component that interprets user requests and three subordinate components that handle flights, hotels, and ground transportation. The top-level component is responsible for routing each request, sequencing the subordinate components, and reconciling their outputs into a final itinerary. The diagram also shows that each subordinate component has its own tool list and own short conversation history that is not shared with the others.
Which architectural pattern does this diagram most closely describe?

• A. A manager and supervisor pattern with shared context, where the top-level agent and the subagents all share a single conversation history that grows as the request flows through the system.
• B. A pipeline pattern, where each component processes the user request in sequence and passes the full conversation history along with the request to the next component in the pipeline.
• C. A manager and supervisor pattern with isolated context per subagent, where the top-level agent coordinates specialized subagents that each maintain their own conversation history and tool list.
• D. A retrieval-augmented pattern, where the top-level component retrieves relevant context from the subordinate components' indexed data stores before generating each response.

Your Claude application produces good responses for typical inputs but struggles with edge cases. You have several labeled examples of edge-case inputs and the desired response for each. You want to use these examples to improve the model's handling of edge cases.
What is the best way to use these examples?

• A. Embed the examples in a database for the model to find during inference.
• B. Add the labeled edge-case examples to the prompt as few-shot examples so the model can learn the pattern.
• C. Train a custom model on the edge-case examples and deploy that custom model in place of the team's current Claude integration.
• D. Tell users to avoid submitting the edge-case inputs to the application by adding warnings in the application's user interface.

You have just shipped a new Claude-powered application to production. The development phase is complete, and the system is now in active use by internal teams.
The next phase of work for this system is...

• A. Retiring the system, since shipping is the final lifecycle stage for any application that reaches production.
• B. Handing the system over to a separate operations team that will manage it independently of the development team going forward.
• C. Operating and maintaining the system, including monitoring, responding to issues, and planning evolution.
• D. Running a formal post-deployment review that assesses the development phase before the team does any further work on the system.