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.
Testing reveals that when source documents are missing certain specifications, the model fabricates plausible-sounding values to satisfy your schema's required fields. For example, a document mentioning only dimensions receives a fabricated "weight: 2.3 kg" in the extraction output.
What schema design change most effectively addresses this hallucination behavior?

• A. Change fields that may not exist in source documents from required to optional, allowing the model to omit them.
• B. Add a "confidence" field alongside each specification where the model self-reports its certainty, then filter out low-confidence extractions.
• C. Add explicit instructions to the prompt stating "only extract information explicitly stated in the document; use placeholder text for missing values."
• D. Implement semantic validation that verifies each extracted value appears in or can be inferred from the source document text.

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.
Your monorepo contains shared coding standards in /docs/standards/ : security-rules.md (for services handling user data), testing-petterns.md (for all packages), and api-conventions.md (for API-facing services). Your 15 packages are organized by feature domain ( /packages/auth/,
/packages/billing/, /packages/notifications/ , etc.) without naming conventions indicating which handle user data or expose APIs. Package maintainers are expected to configure their own local development settings, as they understand their package's domain requirements. Currently, all package CLAUDE.md files duplicate all three standards, applying irrelevant guidance. What's the most effective approach?

• A. Use @imports in each package's CLAUDE.md to reference only the specific standard files relevant to that package, based on the maintainer's domain knowledge.
• B. Create .claude/rules/ files for each standard with YAML frontmatter paths listing every package directory where that standard should apply.
• C. Put all standards in the root CLAUDE.md with override instructions like "ignore security-rules.md when working in packages that don't handle user data."
• D. Create a shared-stendards.md that uses @imports to combine all three standards, then have each package's CLAUDE.md import that combined file.

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 restaurant menus and must output structured JSON with fields for item names, descriptions, prices, and dietary tags. Some menus use inconsistent formatting-- prices as "$12" vs "12.00", dietary info as icons vs text.
What's the most reliable approach?

• A. Use separate extraction calls for each field to ensure consistent handling of each type.
• B. Request multiple extraction attempts per document and select the most common format.
• C. Extract data as-is and normalize formats in post-processing code after Claude returns.
• D. Define a strict output schema and include format normalization rules in your prompt.

A developer wants Claude to explain every reasoning step internally before answering. Why might this request be inappropriate?

• A. Internal reasoning should not be relied upon as an application feature.
• B. Claude requires GPU acceleration.
• C. JSON cannot contain reasoning.
• D. Temperature prevents explanations.

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?

• A. Create a comprehensive specification of every pattern that must not be flagged and include the complete document in the system prompt.
• B. Add general instructions telling Claude to be conservative and report only definite issues.
• C. Use keyword-based post-processing to remove findings containing terms such as "convention,"
  "context-dependent," or "trade-off."
• D. Include few-shot examples containing annotated code snippets that distinguish acceptable project patterns from genuine issues in each category.