New model drop? Before you rewrite your stack, spend 10 minutes pressure-testing it. Here’s a fast, practical checklist you can run on models like Kimi K3—focused on what matters: retrieval, synthesis, and citation fidelity.
Why this matters
Long-context models promise “read the whole doc and reason over it.” In practice, quality varies. A quick, structured test helps you separate marketing from material impact.
For hands-on color, see Simon Willison’s notes on Kimi K3, which inspired this checklist: simonwillison.net.
The 10-minute test plan
- Grounded retrieval (3 min): Paste a long doc or multi-section content. Ask it to extract 5 exact quotes with section/page references and return verbatim citations.
- Synthesis under constraints (2 min): Request a concise 150-word brief with a bulleted risk list, each risk tied to a citation from the source.
- Contradiction/edge scan (2 min): Ask it to find conflicts, caveats, or outliers in the source, with direct quotes.
- Structure from chaos (2 min): Have it build a clean table (columns you define) from messy source text—then spot-check 2 random rows against the original.
- Instruction sensitivity (1 min): Repeat one task with stricter formatting rules (e.g., JSON schema). Check adherence, not just answers.
Copy-paste prompts
Use these with any long-context model. Paste your source first, then run:
- Grounded quotes: “Return exactly 5 direct quotes that answer: [your question]. For each, include exact page/section and a copy-pastable citation key. Do not paraphrase.”
- Synthesis: “In ≤150 words, summarize the key argument. Then list 3 risks in bullets. After each bullet, include [CITE: citation-key] that maps to a direct quote.”
- Contradictions: “List up to 5 contradictions, caveats, or limitations in the source. For each, include a verbatim quote and location.”
- Table: “Produce a markdown table with columns: [A, B, C]. Fill only from the provided text. After the table, list the 2 most uncertain rows and why.”
- Schema adherence: “Output valid JSON only matching this schema: {\”finding\”: string, \”quote\”: string, \”location\”: string}. No extra keys.”
What good looks like
- Verbatim, traceable quotes with precise locations
- Concise synthesis that clearly separates facts from model commentary
- Tables that align row-by-row with source data
- Strict format obedience when asked (JSON, tokens caps, bullet counts)
Red flags
- Vague references like “the document says” without location
- Paraphrases presented as quotes
- Invented rows or columns in tables
- Ignoring explicit formatting or schema instructions
Pro tip: Evaluate with a fixed harness
Keep a simple, reusable evaluation set (same doc, same prompts). That lets you compare new models apples-to-apples over time. For broader evaluation guidance, see Stanford’s HELM project: crfm.stanford.edu/helm.
Sources
- Simon Willison: Hands-on notes with Kimi K3 – simonwillison.net
- HELM: Holistic Evaluation of Language Models – crfm.stanford.edu/helm
Takeaway
Don’t trust model demos. Trust fast, grounded checks that reveal retrieval quality, synthesis clarity, and citation fidelity—before you commit.
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