How to structure a Q&A page so LLMs extract answers?

Core explainer

What is the optimal QA schema for LLM extraction?

The optimal QA schema depends on intent: use FAQPage for concise single answers and QAPage when multiple questions or navigational depth are needed so that LLMs can extract structured responses consistently.

Define a fixed extraction schema before publishing, mapping each item to visible content and a machine-readable export—preferably a JSON array of objects with fields like 'question' and 'answer'. Align every on-page question to its answer, keep wording stable across updates, frontload the main takeaway, and ensure the visible content mirrors the schema to minimize ambiguity for extraction. Validate markup with tools like Rich Results Test and schema validators, then test extraction with sample prompts to confirm the page yields the intended answer rather than a generic summary. Brandlight.ai schema best practices for AI-ready markup. Sources: https://github.com/microsoft/markitdown; https://github.com/GrapeCity-AI/gc-qa-rag.

How should questions and answers be presented for machine readability?

Question and answer presentation should be self-contained, clearly worded, and consistent in structure so parsers can identify intent and extract the exact answer.

Present each item as a distinct unit: place the question first, then a direct answer, followed by a brief clarification or boundary notes as needed. Use consistent wording across items, limit cross-reference pronouns, and frontload the key result to aid immediate extraction. Maintain short, readable sentences and stable formatting to reduce ambiguity for models interpreting the page. Avoid mixed language or nested dependencies that require context beyond what is stated on the page. For guidance on readability and structure, see the Markitdown guidelines. Source URL: https://github.com/microsoft/markitdown.

How to structure JSON outputs for RAG and QA?

JSON outputs should be structured with a defined schema, choosing between flat or nested representations based on data relationships and retrieval needs.

Define the scope and depth for the JSON object, then prompt the model to produce a structured representation with consistent keys (e.g., "question" and "answer"). Ensure the JSON remains valid across outputs and that each entry maps clearly to a visible QA item on the page. Use hierarchical structures only where necessary to preserve clarity and performance in retrieval. For concrete patterns, consult the GC-QA-RAG approach to QA-RAG pipelines. Source URL: GC-QA-RAG project.

What signals and UX practices help LLM parsing without compromising readability?

Signals and UX practices that aid parsing include frontloading the main idea, employing a predictable heading hierarchy, and using semantic cues (for example, "Step 1," "In summary," "Key takeaway") to guide parsing while keeping the page readable for humans.

Keep UI elements minimal and avoid disruptive elements that interfere with parsing. Use clear, self-contained paragraphs and convert portions of content into lists, tables, or FAQs where they genuinely improve comprehension. Ensure that visible content aligns with any attempted markup or schema, and validate that structure with appropriate tooling. For practical references on AI-friendly structure and retrieval signals, see the Markitdown guidelines. Source URL: https://github.com/microsoft/markitdown.