Contextualizing AI Moderators: Putting Purpose Behind the Probe
In this webinar, we dive into how context elevates AI moderation from generic to insightful. Rather than relying solely on fixed prompt templates, contextualizing your AI moderator gives it the nuance to ask more relevant, tailored questions.
Why Context Matters in AI Moderation
AI moderators tend to operate in a vacuum if you don’t feed them sufficient background. Without context, they may ask questions that are:
- Out of sync with the interview’s goals
- Irrelevant to the participant’s role or experience
- Tone- or domain-misaligned
By contrast, when your AI has context — such as the participant’s persona, project constraints, or brand voice — it can:
- Tailor phrasing to feel more natural
- Choose relevant follow-ups over generic probes
- Avoid repeating known information
- Steer the conversation toward high-value insights
Three Key Context Layers to Inject
- Participant Profile & Role
Who is the participant? What background or expertise do they have? Provide these details so the AI frames questions at the right level (novice, expert, domain-specific). - Research Goals & Themes
What is your study trying to uncover? Whether it’s usability, motivation, workflow, or adoption — giving the AI clarity on your objectives sharpens its probing direction. - Brand Tone & Constraints
Knowing your brand’s style (formal, conversational, curious) and any domain constraints (e.g. regulatory, ethical) helps ensure the AI’s questions stay on-brand, both in voice and content.
How to Embed Context Into AI Moderation
- Pre-prompt your AI with a short “context packet” (e.g. “You are interviewing a product manager about AI fairness in ad ranking.”)
- Use dynamic context injection during interview: when a participant mentions something new, the AI adapts future probes accordingly
- Limit over-contextualization: too many context signals can overwhelm the model. Use only the most relevant ones.
- Iteratively refine based on transcripts: after pilot interviews, see where contextual gaps led to mismatches, then adjust.
FAQs
Contextualizing an AI moderator means giving it background information — like who the participant is, what the study’s about, and what tone or brand voice to use. This helps the AI ask smarter, more relevant follow-ups rather than relying solely on generic prompts.
Start with three key layers: participant background (role, expertise, company), study objectives (themes, hypotheses), and brand or tone guidance. You can also include constraints — such as privacy rules or specific topics to avoid — to ensure responsible moderation.
Without context, AI tends to ask surface-level or repetitive questions. Adding context lets it interpret responses with more nuance, stay aligned with your study goals, and probe deeper into areas that matter most to your research.
Look for signs in your transcripts: are follow-ups specific and on-topic? Is the tone appropriate to your brand? Are probes deepening insights rather than repeating information? If not, refine your context packet and run another pilot. Iteration is key.
