Query Vault

Every wiki article has frontmatter: title, type, domains, status, created, updated, sources, related. Articles use dense [[wikilinks]]. No tags.

Query types:

• Factual lookup — "what is RAG?", "who is Andrej Karpathy?" → one or two articles, direct answer
• Comparison — "compare agent frameworks", "GPT-4 vs Claude" → multiple articles, structured table
• Research question — "state of AI safety", "how do MoE models work?" → domain index + multiple articles, narrative synthesis
• Output request — "create slides on agents", "write a report on evals" → research then generate artifact </context>

Parse what the user is asking and classify it:

• Is this a factual lookup? → find the one or two most relevant articles
• Is this a comparison? → identify both sides, find articles for each
• Is this a research question? → check the relevant domain index first, then read multiple articles
• Is this an output request? → determine format (research doc, slides), then research first

For ambiguous queries, make a reasonable assumption and proceed — don't ask clarifying questions unless the query is genuinely impossible to interpret.

Step 2: Navigate the wiki

1. Read wiki/_index.md to find relevant articles by scanning titles and summaries. Identify 3-10 candidates.
2. Check the most relevant domain index for curated narrative context and to discover related articles you might have missed.
3. Read the top 2-5 wiki articles in full. Follow [[wikilinks]] one level deep when they point to directly relevant content.
4. If the wiki doesn't have enough information, say so clearly. Name what's missing and suggest what sources could be added to raw/ to fill the gap (e.g., specific papers, blog posts, documentation).

Do not read every article speculatively. Use the index to navigate efficiently.

Step 3: Synthesize the answer

• Ground every claim in wiki articles, citing with [[wikilinks]]
• Be honest: distinguish what the wiki covers from what it doesn't
• For factual lookups: 1-3 paragraphs, direct and dense
• For comparisons: lead with a summary, then use a structured table, then nuance
• For research questions: narrative with clear sections, citations throughout
• For output requests: research first (steps 1-2), then generate the artifact

Step 4: Choose output format

Match output format to query complexity:

• Quick answer (factual lookup, simple comparison) → respond in chat only, do not save
• Research answer (multi-article synthesis, research questions) → save to outputs/research/YYYY-MM-DD-topic.md:

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