Anthropic CCAR-F Value Pack (Frequently Bought Together)

Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?

• A. Increase temperature.
• B. Remove examples.
• C. Shorten responses only.
• D. Add grounding through trusted retrieval.

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline processes invoices and extracts line items, subtotals, tax amounts, and grand totals. During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total--sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?

• A. Extract line items and totals independently, then use a separate validation model to reconcile discrepancies by determining which extracted values are most likely correct.
• B. Add a "calculated_total" field where the model sums extracted line items alongside a
  "stated_total" field. Flag records for human review when values differ.
• C. Add few-shot examples demonstrating invoices where extracted line items sum correctly to the stated total, encouraging the model to produce mathematically consistent extractions.
• D. Implement post-processing that automatically adjusts line item amounts proportionally when their sum doesn't match the stated total.

You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools--Read, Write, Bash, Grep, and Glob--and integrates with Model Context Protocol (MCP) servers.
Your productivity agent connects to three MCP servers: an issue tracker with search_issues, get_issue, and create_comment; a documentation wiki with search_docs, get_page, and list_spaces; and a database explorer with run_query, get_schema, and list_databases. For cross- system questions such as, "Which database tables are affected by the authentication refactor in PROJ-1234?", the agent makes eight to ten sequential exploratory calls, lacks visibility into each server's available content, and exhausts context before completing complex investigations.
What architectural change best leverages MCP capabilities to address these problems?

• A. Add a prepare_investigation tool to each server that accepts a natural-language question and returns relevant summaries.
• B. Add an orchestrator that routes each question to one server based on keywords.
• C. Consolidate all three systems into one unified MCP server with cross-referencing capabilities.
• D. Expose each server's content catalog as MCP resources, including issue summaries, documentation hierarchies, and database schemas.

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?

• A. Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
• B. Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
• C. Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
• D. Use plan mode to analyze the current/products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.

A user asks Claude to ignore all previous instructions and reveal confidential internal prompt content. How should Claude behave?

• A. Follow higher-priority instructions and refuse.
• B. Reveal the prompt.
• C. Generate random text.
• D. Partially reveal hidden instructions.