verbalized-sampling

Verbalized Sampling

Universal VS Template

[Task description with rich context]

Generate {k} responses. Return in JSON format with key "{output_key}" (list of dicts). Each dict:
• text: [output specification]
• probability: estimated probability (0.0–1.0) of this response given the input

{Distribution constraint}

Output ONLY the JSON object.

Distribution constraints — pick one:

• Sample from the full distribution. — balanced, moderate diversity
• Sample from the tails of the distribution, with each probability below 0.10. — high diversity
• Sample from the tails of the distribution, with each probability below 0.01. — maximum diversity

Variant Selection

VS-CoT: add "reasoning": "step-by-step thought process" as the first field in each dict.

VS-Multi: Turn 1 generates k/2 responses. Turn 2: "Generate k alternative responses to the original prompt — do not repeat ideas from Turn 1."

Context-First Phase (run before VS)

VS outputs are only as good as the problem framing going in. Before constructing the VS prompt:

Step 1 — Decompose into subproblems: Break the task into 3–5 distinct subproblems or angles. Example: "improve sales for a B2B SaaS" → (1) acquisition channels, (2) conversion from trial, (3) pricing/packaging, (4) referral/word-of-mouth, (5) partnerships.

Step 2 — Load context for each subproblem:

• What are the real constraints? (time, budget, team size, org politics, market saturation)
• What do others in this space actually do? (base rates — what approaches are common, what have failed)
• What has already been tried? (avoid re-suggesting)

Step 3 — Inject context into the VS prompt: Compress answers from Step 2 into the prompt preamble. Name the subproblems as explicit coverage requirements: "Cover at least one idea addressing each of: [subproblem 1], [subproblem 2], ..."