Session Replay Assistant Prompts for Product Teams
A session replay assistant is most useful when the prompt asks for observable behavior, representative sessions, and a verification path. It is least useful when the prompt asks AI to guess motive, decide priority, or explain a broad metric change without enough evidence.
This guide gives product teams public prompt patterns for AI-assisted replay review. It does not describe Monolytics’ internal Assistant prompts, scoring system, ranking logic, or implementation details.
Last reviewed: July 1, 2026. Replay can show observable behavior and context. It does not prove exact user motive, root cause, frustration level, or business impact by itself.
Prompt structure
Use this structure before asking a replay assistant for help:
| Part | What to include | Example |
|---|---|---|
| Journey | Page, route, flow, event, or product step | Pricing page, signup form, onboarding setup |
| Failed outcome | What did not happen | Did not start trial, did not submit, did not activate |
| Observable behavior | What replay can show | Dead clicks, loops, hesitation, retries, exits, errors |
| Segment | Which sessions should count | Mobile, paid search, new accounts, trial users |
| Evidence request | What the assistant should return | Representative sessions, repeated patterns, comparison questions |
| Verification rule | What the team will check manually | Compare successful sessions and label confidence |
This keeps the prompt grounded in replay evidence.
For a deeper question-framing guide, see product questions to ask your session replay assistant.
Prompt pattern: find failed sessions
Use when the team knows the path and outcome.
Prompt:
Find sessions where [segment] reached [journey] but did not complete [failed outcome]. Return the visible behavior around the failure, representative sessions, and any repeated patterns. Keep the wording behavioral and do not infer motive.
Example:
Find mobile signup sessions from paid search where users started the form but did not submit. Return visible behavior around validation, privacy checks, retries, exits, and representative sessions. Do not infer motive.
Good output:
- sessions that match the path;
- repeated visible behavior;
- representative examples;
- gaps that need manual review.
Prompt pattern: compare failed and successful sessions
Use when the team may be overreading failed sessions.
Prompt:
Compare [failed session group] with [successful session group] from the same segment. Focus on visible behavior before [decision point]. Return the differences that repeat and the sessions that support them.
Example:
Compare mobile pricing visitors from comparison content who did not start trial with similar visitors who did. Focus on plan comparison, proof checks, FAQ use, CTA interaction, and exits.
This prompt helps separate friction from normal evaluation.
Prompt pattern: inspect a possible UX issue
Use when the assistant surfaced a UX issue candidate.
Prompt:
Review sessions where users show [observable behavior] near [UI element or step]. Return whether the behavior repeats, whether successful sessions show the same behavior, and what evidence is missing before this becomes a UX finding.
Example:
Review sessions where users repeatedly click the plan-details row on mobile pricing. Return whether the behavior repeats, whether successful sessions do the same thing, and what evidence is missing before this becomes a UX finding.
Use how to validate AI-surfaced UX issues before turning the answer into a fix.
Prompt pattern: inspect a possible bug
Use when replay shows failed interactions.
Prompt:
Find sessions with [bug-like symptom] on [route or flow]. Return the visible symptom, environment context, representative sessions, recovery behavior, and whether the pattern is lead, repeated pattern, segmented pattern, supported finding, or unclear.
Example:
Find sessions with repeated taps on the signup submit button where no visible progress happens. Return device context, validation state, representative sessions, recovery behavior, and confidence level.
Use AI bug triage from session replay evidence when the answer needs to become an engineering triage note.
Prompt pattern: ask for confidence, not certainty
Use when a finding might be overclaimed.
Prompt:
Classify this replay finding as lead, repeated pattern, segmented pattern, supported finding, or refuted/unclear. Explain what evidence supports the label, what evidence is missing, and what the next small action should be.
Example:
Classify the finding “pricing visitors are confused by plan limits” using replay evidence only. Explain whether it is a lead, repeated pattern, segmented pattern, supported finding, or unclear. Do not claim motive unless another signal supports it.
Use the session replay evidence confidence matrix for the definitions.
Prompt pattern: find feedback opportunities
Use when replay shows behavior but not reason.
Prompt:
Find sessions where [segment] shows [ambiguous behavior] before [failed outcome]. Return the observable behavior, representative sessions, and one targeted feedback question that could clarify the decision moment.
Example:
Find onboarding sessions where new accounts loop between setup and docs before activation. Return visible behavior, representative sessions, and one targeted feedback question that could clarify whether permissions, effort, or unclear next steps are the issue.
The assistant should not decide the reason. It should help the team ask a better follow-up question.
Prompt pattern: summarize without overclaiming
Use when stakeholders need a short note.
Prompt:
Summarize the observed behavior in this review set. Include the product question, segment, repeated behavior, representative sessions, confidence level, evidence limit, and next small action. Do not infer exact user motive, root cause, or business impact.
Example output format:
| Field | Fill it in |
|---|---|
| Product question | What decision are we trying to make? |
| Segment | Which sessions were reviewed? |
| Observed behavior | What happened in replay? |
| Representative sessions | Which sessions support the pattern? |
| Confidence | Lead, repeated pattern, segmented pattern, supported finding, unclear |
| Limit | What does the evidence not prove? |
| Next action | Fix, instrument, survey, test, monitor, or postpone |
Use when not to trust AI session summaries when the summary sounds stronger than the evidence.
Prompts to avoid
Avoid prompts that ask the assistant to decide:
- “Why is conversion down?”
- “What do users hate?”
- “What should we build next?”
- “Is our pricing too expensive?”
- “What is the root cause?”
- “How much revenue are we losing?”
- “Which feature should we remove?”
Rewrite them into observable replay questions:
| Avoid | Rewrite |
|---|---|
| Why is conversion down? | Find sessions where pricing visitors compare plans but do not start trial |
| What do users hate? | Find sessions with repeated retries, dead clicks, loops, or exits near the decision point |
| What should we build next? | Find repeated sessions where users attempt an unsupported action |
| Is pricing too expensive? | Find sessions where visitors inspect plan limits, proof, and FAQs before leaving |
| What is the root cause? | Return visible behavior and supporting signals needed before root-cause claims |
The rewrite does not make the assistant less useful. It makes the answer more reviewable.
How Monolytics fits
Use Monolytics Assistant session search for repeated patterns across many sessions. Use Monolytics Records when the team already knows the route, event, source, or failed path.
Then use the AI session replay analysis workflow to review the answer, the AI session replay analysis checklist as the gate before action, and the session replay evidence review template to document the final finding.
For the public product path, use See every bug to understand the AI-assisted replay workflow and Monolytics pricing when the team needs more replay volume, AI session search, surveys, or retention.
Related guides
- When not to trust AI session summaries for summary guardrails.
- AI funnel analysis with session replay for converting broad funnel questions into bounded review questions.
- AI customer journey analysis from session replay for journey questions that span multiple stages.
- How to validate AI-surfaced UX issues for UX issue candidates.
- AI bug triage from session replay evidence for bug-like findings.
- Privacy-safe AI session replay analysis before prompting around sensitive flows.