Autoresearch + introspection is the emerging pattern for getting trustworthy answers from AI. Inspired by Latent Space’s deep dive, here’s a fast, practical workflow you can run today.
What it means
Autoresearch: let the model plan searches, read sources, and draft findings with citations.
Introspection: force the model to critique its own reasoning, verify claims, and revise before you trust the output.
A 60‑minute autoresearch sprint
- 00–05 min — Frame the decision. Prompt: “You are my research lead. Clarify the decision to make, who it impacts, time horizon, and what would change based on this research. Output: 3–5 success criteria.”
- 05–10 min — Break into sub‑questions. Ask for a numbered list of sub‑questions and the evidence needed to answer each (data, papers, benchmarks, quotes).
- 10–20 min — Query and collect. Have the model propose 10 search queries, then retrieve top sources. Require a citation table: title, link, type (paper/blog/report), key finding, confidence.
- 20–30 min — Read for claims, not vibes. Ask for 1–2 sentence takeaways per source with direct quotes and page/section references.
- 30–40 min — Synthesize first pass. Prompt: “Write a 300–400 word brief. Use only grounded claims from the table. Include a ‘What we don’t know’ section.”
- 40–50 min — Introspection pass. Prompt: “List all claims. For each: evidence link, exact quote, and a 0–1 confidence. Flag contradictions and unsupported statements.”
- 50–60 min — Revise and decide. Prompt: “Revise the brief to remove or qualify low‑confidence claims. End with a decision recommendation tied to success criteria.”
Introspection loops that actually help
- Self‑critique before finalizing: Have the model generate a “reasons I might be wrong” list and fix the top 3 issues.
- Verifier model check: Ask a second model to evaluate each claim for support vs. speculation.
- Grounded answers only: Force inline citations next to every claim (no bibliography‑only citations).
- Contradiction scan: Compare takeaways across sources and call out conflicts explicitly.
Suggested tooling
- LLM: Any top model with system prompts and long context.
- Search/Retrieval: Your preferred web search API or RAG stack; save all URLs.
- Citations: Track a simple table (title, link, quote, claim, confidence) in Sheets or Notion.
Why this works
Introspective loops reduce ungrounded synthesis and push the model to check itself. Techniques like self‑reflection and multi‑step reasoning have empirical support in research such as Reflexion and Tree of Thoughts.
Takeaway
Let the model do the legwork, but make it prove every claim. Autoresearch speeds you up; introspection keeps you honest.
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