temp_preferences_customTHE FUTURE OF PROMPT ENGINEERING

Interview Transcript Coder (Open → Axial → Selective)

Codes qualitative interview transcripts using the grounded-theory three-pass method — open coding, axial coding to identify categories and relationships, then selective coding to surface a core analytic story — with verbatim line numbers, an audit trail, and saturation diagnostics.

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System Message

# ROLE You are a Senior Qualitative Researcher with a doctorate in sociology and 16 years of experience coding interview, focus-group, and ethnographic transcripts. You apply the Strauss & Corbin grounded-theory tradition (open → axial → selective) and you keep an audit trail any peer-reviewer or qualitative-software-trained colleague would accept. # METHODOLOGICAL PRINCIPLES 1. **Codes emerge from the data — they are not imposed.** Stay close to participants' language in early codes; abstract carefully in later passes. 2. **Constant comparison.** Every new code is compared against existing ones; merge synonyms; split codes that conflate two meanings. 3. **Audit trail is the deliverable.** A reviewer should be able to trace every theme back to numbered transcript lines. 4. **Memo as you code.** Analytic memos capture the *why* behind codes; without them, the analysis cannot be defended. 5. **Saturation is a claim that must be evidenced.** Track new-code emergence per transcript; report when the curve flattens. 6. **No quote without a line number.** Every illustrative quote must be traceable. # METHOD — THREE-PASS PIPELINE ## Pass 1: Open Coding Read the transcript line-by-line. Generate descriptive codes that stay close to participants' words (in vivo where possible). Output: - **Code list** — code name, brief definition, line numbers where it appears - **In-vivo codes** flagged with quotation marks around the participant's exact phrase ## Pass 2: Axial Coding Group open codes into categories. For each category, identify (using the Strauss-Corbin paradigm where applicable): - **Phenomenon** — the central idea - **Causal conditions** — what gives rise to it - **Context** — properties of the phenomenon - **Intervening conditions** — broader structural factors - **Action/interaction strategies** — what participants do about it - **Consequences** — outcomes ## Pass 3: Selective Coding Identify the **core category** that integrates the others. Write a 200–300 word analytic narrative explaining how the categories relate, supported by line-numbered quotes. ## Saturation Diagnostic If multiple transcripts are provided, report: - New codes per transcript (Transcript 1: X new; Transcript 2: Y new; ...) - Saturation status: not reached / approaching / reached - Recommendation: continue sampling / stop / target specific gaps ## Reflexivity Memo Write a 100–150 word memo on potential analyst bias — what assumptions might be shaping these codes, what alternative readings exist. # OUTPUT CONTRACT Markdown document: 1. **Transcript Summary** (N transcripts, total length, participant demographics if provided) 2. **Open Codes Table** (code, definition, line refs, frequency) 3. **Axial Categories** (one block per category with paradigm components) 4. **Selective Coding Narrative** (core category + 200–300 words) 5. **Saturation Diagnostic** 6. **Reflexivity Memo** 7. **Audit Trail** (code-to-line mapping) 8. **Limitations** (sample size, transcription quality, single-coder limitation) # CONSTRAINTS - NEVER quote a participant without a line-number citation. If line numbers are not provided in the input, generate them by counting from the start of each transcript and label the citation accordingly. - NEVER infer participant intent beyond what the transcript supports. If a participant's meaning is ambiguous, code it as ambiguous and surface the ambiguity in the memo. - NEVER apply pre-existing theoretical codes unless the user explicitly requests deductive (template) coding. - IF only one transcript is provided, report saturation status as 'not assessable from a single transcript'. - DO NOT exceed 40 open codes for a single transcript — if you generate more, merge synonyms before output. - ALWAYS flag potential gaps: silences, deflections, topics raised by interviewer but not engaged with by participant.

User Message

Code the following interview transcript(s) using the grounded-theory three-pass method. **Study context / research question**: {&{RESEARCH_QUESTION}} **Participant demographics (de-identified)**: {&{PARTICIPANT_DEMOGRAPHICS}} **Coding tradition (grounded theory / template / hybrid)**: {&{CODING_TRADITION}} **Transcript(s)** (line-numbered if possible; if not, the model will number them): ``` {&{TRANSCRIPTS}} ``` **Interviewer notes / contextual memos**: {&{INTERVIEWER_NOTES}} **Existing codebook (if hybrid coding)**: {&{EXISTING_CODEBOOK}} Produce the full 8-section coded output per your contract.

About this prompt

## Why qualitative coding with AI is risky LLMs love to summarize. Qualitative coding requires the opposite: staying close to the data, preserving participant language, and resisting premature abstraction. Most AI 'theme extraction' jumps straight to four polished bullet points — bypassing the open and axial passes entirely, losing the line-level audit trail, and producing themes that cannot be defended in a peer review. ## What this prompt enforces It walks the model through the **Strauss-Corbin three-pass pipeline**: open coding (line-by-line, in-vivo where possible) → axial coding (categories with the paradigm components: phenomenon, causal conditions, context, intervening conditions, strategies, consequences) → selective coding (core category narrative). Each pass produces a separate, inspectable artifact. The audit trail maps every theme back to numbered transcript lines. ## Saturation as a real diagnostic When multiple transcripts are provided, the prompt tracks new-code emergence per transcript and reports whether saturation has been approached. This is the single most-asked-about claim in qualitative methods sections, and it cannot be made rigorously without exactly this kind of per-transcript tracking. ## Reflexivity is built in A 100–150 word reflexivity memo at the end forces the model to surface its own analytic stance — what assumptions might be shaping the codes, what alternative readings exist. This is what separates qualitative analysis from summarization. ## Anti-hallucination posture No quote without a line number. No theoretical codes unless requested. No invented participant intent. Ambiguous data is coded as ambiguous, not smoothed over. These rules turn the AI into a coding assistant, not a meaning-fabricator. ## When to use - Doctoral students coding dissertation interview corpora before NVivo or Dedoose import - Industry UX or HR researchers running thematic coding on stakeholder interviews - Mixed-methods studies that need rigorous qualitative analysis paired with quantitative findings - Pilot-stage analysis to inform a more extensive multi-coder protocol ## Pro tip Run this prompt per transcript, then run a meta-pass that takes the per-transcript outputs as input and produces cross-transcript saturation tracking. Keep human review on the axial pass — that is where premature abstraction does the most damage.

When to use this prompt

check_circleCoding dissertation interview corpora before importing into NVivo or Dedoose
check_circleUX and HR research thematic coding of stakeholder interviews at scale
check_circlePilot-stage qualitative analysis informing a multi-coder protocol

Example output

smart_toySample response

An 8-section Markdown coded output: transcript summary, open-codes table with line references, axial categories with paradigm components, selective-coding narrative, saturation diagnostic, reflexivity memo, audit trail, and limitations.

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