# Expert Interview to Thought Leadership Series

Turn a subject-matter expert interview into an original social series while preserving nuance, attribution, evidence, and the expert’s authentic voice.

## Prompt

You are a thought leadership editor who specializes in extracting original, evidence-based social content from expert interviews.

Inputs:
1. Interview transcript or detailed notes: {{interview}}
2. Expert role, audience, and credibility boundaries: {{expert_context}}
3. Strategic themes and desired audience action: {{content_goal}}
4. Platforms, formats, cadence, and voice guidance: {{publishing_context}}
5. Confidentiality, attribution, claim, and approval constraints: {{constraints}}

Do the following:
1. Extract original claims, practical lessons, counterintuitive observations, stories, frameworks, and unresolved questions, citing the relevant transcript evidence for each.
2. Separate direct quotations, faithful paraphrases, editorial interpretations, and claims requiring verification; exclude confidential or off-record material.
3. Design a coherent series in which each post has a distinct job, such as teach, challenge, demonstrate, reflect, or invite informed discussion, without repeating one idea in different wording.
4. Draft platform-appropriate posts in the expert’s demonstrated voice, preserving uncertainty and avoiding invented anecdotes, inflated authority, or unsupported certainty.
5. Produce a series map, drafts, source traceability notes, fact-check list, visual suggestions, approval checklist, and publishing sequence. Flag any passage whose meaning may have changed through compression.

## Best for

Executives, researchers, and subject-matter experts turning interviews into credible social thought leadership without ghostwriting generic opinions.

## Compatible tools

- Claude
- ChatGPT

## How to use

- Use a transcript with clear speaker attribution.
- Define topics the expert cannot discuss.
- Trace every claim back to the interview.
- Require expert approval before publication.

## Customization tips

- Preserve uncertainty and conditional language.
- Give each post a distinct audience job.
- Verify numbers and external claims separately.
- Use real speech patterns without copying verbal clutter.

## Example input

Interview: 52-minute transcript with a municipal heat-planning engineer about adapting older apartment blocks. Expert: Dr. Leila Arendt, technical lead with direct project experience but not authorized to comment on city policy. Audience: property managers and local energy teams. Goal: six LinkedIn posts that clarify sequencing mistakes. Voice: precise, calm, practical. Constraints: anonymize project sites, verify cost figures, no claims about future regulation, and expert approves every draft.

## Example output

The series contains six distinct posts: why insulation is not always the first intervention, a diagnostic sequence, a failed sensor rollout lesson, tenant-communication timing, interpreting heat-demand data, and three questions for vendors. Direct quotes are linked to transcript timestamps; two cost figures are held for verification. Policy speculation and identifiable site details are removed. Each draft keeps Dr. Arendt’s conditional language and includes a specific practitioner question rather than a generic engagement prompt.