Tune Wave discovery

Tuning starts with Product Area settings. Product Areas define the goals, scope, sources, contrasts, metrics, and instructions that steer what Amplitude looks for and how it judges quality. If generated Opportunities feel misaligned, the first place to tune is the Product Area.

Feedback compounds inside that Product Area. Direct comments, ratings, status changes, edits, and triage decisions all help Amplitude improve future recommendations for the same Product Area.

What you can tune

Tune these settings when a Product Area produces Opportunities that feel misaligned, repetitive, too small, or too broad. In practice, teams vary Product Areas along a small set of dimensions: scope and exclusions, goal tags, target metrics, success criteria, insight sources, opportunity mix, and three instruction lanes (plan, investigation, and coding agent).

Define a strong focus area

A strong focus area gives the system a clear search space. It should name the user journey or product surface and describe what outcome matters.

Good focus areas:

• AI Feedback: Covers the full source-to-insight loop, including source connection, ingestion quality, theme generation, insight review, feedback triage, and repeated team use. Excludes unrelated core analytics funnels unless they directly affect the feedback workflow.
• Dashboards: Covers first dashboard creation, chart edits over time, dashboard retention, sharing, exports, and recurring reporting workflows. Excludes deep chart-query authoring unless it directly blocks dashboard creation or ongoing dashboard use.
• Checkout: Covers cart-to-payment completion, payment errors, recovery flows, purchase confirmation, and guardrail metrics such as latency. Excludes marketing landing pages and post-purchase retention unless they directly affect checkout completion.
• Session Replay for support: Covers replay discovery, support-led debugging, replay sharing, issue reproduction, and follow-up analysis. Excludes general replay exploration unless it directly supports support workflows.

Weak focus areas:

• Growth.
• The dashboard.
• Improve UX.
• Everything related to AI.

If a focus area feels broad, split it. For example, instead of one Product Area for "Onboarding", create separate areas for "First data connection", "First chart creation", and "Invite teammates".

Add contrast areas

Contrasts are nearby areas that are intentionally out of scope. They help the system distinguish useful opportunities from distracting ones.

Use contrasts when the Product Area borders another team, workflow, or metric.

Examples:

• For Checkout, include payment submission, error recovery, and purchase confirmation. Exclude marketing landing pages and post-purchase retention.
• For Chart Builder, include chart creation, save flows, query latency, and first useful visualization. Exclude dashboard layout and sharing unless they directly block chart creation.
• For AI Feedback, include source connection, ingestion reliability, insight quality, and repeated team use. Exclude unrelated core analytics funnels.

Tune the opportunity mix

Opportunities can represent different types of work. To shape the mix, open Product area settings > Opportunity Types, rank the types in the order Wave should focus on them, and turn off any type Wave should skip. Triage feedback then refines the mix.

The ranking expresses relative preference; it doesn't guarantee an exact percentage of each type. Drag to reorder the types, or use the reorder control and arrow keys.

Match the mix to product maturity

For early products, rank New Feature and Strategic Bet higher. Early teams benefit from broader exploration and bigger swings.

For mature, high-volume surfaces, rank Quality Improvement and Bug Fix higher, and ask for experiments and careful rollout plans. Mature teams usually need smaller, safer, more measurable changes.

For mid-maturity products, keep a balanced mix: some quality improvements, some bug fixes, and a few larger bets.

Select the right sources

Open Configure > Sources to enable sources and constrain their inputs. Enabling a source and defining its scope are separate choices. Go to Connect context for the source controls and external MCP connections.

Sources determine what the system can learn. A Product Area with only analytics may find metric movement but miss the user experience behind it. A Product Area with only feedback may find sentiment but miss scale.

Use this guidance:

• Analytics for volume, conversion, retention, and segment-level impact, including Autocapture signal when custom instrumentation is incomplete.
• Session Replays for how the problem appears in real user behavior.
• AI Feedback for complaints, feature requests, and qualitative themes.
• Experiments for what teams have already tried and what moved metrics.
• Amplitude Agents for ongoing domain-specific summaries from specialized and custom agents.
• Web Vitals for technical performance and page experience signals.
• Agent Analytics for how people use AI-powered products, plus their quality and performance.
• Competitive Analysis for market-aware ideas and comparable workflows.
• PR Reviews when code review activity can reveal delivery friction or implementation risk.
• External sources for MCP-connected internal data sources, proprietary systems, and workflows.
• Code repositories for execution plans that point to real implementation locations.

For high-confidence opportunities, look for at least two independent sources. For example, pair a funnel drop-off with session replay evidence, or pair a feedback theme with a supporting behavior trend.

Use metrics and success criteria as alignment anchors

Target metrics tell Amplitude how to score and frame opportunities. Each Product Area should include:

• Primary metrics that define success.
• Secondary metrics that help diagnose why the primary metric moves.
• Guardrail metrics that shouldn't regress.

Pair metrics with a short success criteria statement so agents optimize for the outcome you care about, not only a chart number. For example: "Increase paid subscription conversion and subscriber return without increasing checkout abandonment."

Avoid using only one metric. A single conversion metric may produce narrow recommendations that ignore quality, latency, support burden, or downstream retention.

Write durable instructions

Product Areas give you three instruction lanes, in Product area settings > Investigation Instructions: Investigation, Plan, and Coding agent. To open them, open the Product Area and click Configure. Fill each lane with stable guidance, not temporary tasks. Temporary requests belong in a focused discovery run, Wave Chat, or a comment on a specific Opportunity.

Plan instructions

Use Plan to guide how Wave turns a finding into a proposed solution. Keep the Product Area charter and boundaries in the Product Area overview. Plan instructions describe:

• Which solution approaches to consider.
• Constraints and the evidence needed to justify an approach.
• What a complete specification includes, such as acceptance criteria and validation.
• Preferences for how to frame, size, or roll out opportunities.

Good plan instructions:

• Cover the paid subscription lifecycle from paywall exposure through purchase completion and subscriber return. Treat repeat content consumption as a separate Product Area unless it directly explains retention.
• Prefer low-effort, reversible changes before high-complexity interventions. Include an experiment or rollout plan for mature, high-volume flows.
• Don't recommend instrumentation-only work. Prefer ambitious product changes when evidence supports them.

Investigation instructions

Use investigation instructions to steer where agents look and what they compare. Strong investigation text usually covers:

• Key journeys or funnels to analyze.
• Signals, segments, or platforms worth comparing.
• Questions the next discovery run should answer.
• Measurement gaps or known confounders to separate from product friction.

Good investigation instructions:

• Explore where eligible readers fail to become subscribers and where new subscribers fail to return after purchase. Compare platform, paywall state, content type, acquisition source, and lifecycle stage.
• Focus on the full daily task loop: session open, task creation, edit and delete flows, and the All Tasks Completed moment. Treat Reset All Data spikes as a frustration guardrail.
• Structure investigation across UI lifecycle, agent tool-call quality, and multi-surface entry points such as chat or MCP. Keep adjacent products out of scope unless they block this area.

Coding agent instructions

Use coding agent instructions when you connect repositories and want implementation handoffs to land cleanly. Strong coding-agent text usually covers:

• Which repos, packages, or services own the surface.
• How to trace a behavioral issue back to specific code paths.
• Preferred change style: minimal fixes, rollback steps, and metric checks after deploy.
• Validation criteria before marking work complete.

Good coding agent instructions:

• Frontend lives in apps/voc and packages/voc-ui. Backend lives in server/packages/klassy. Prefer targeted fixes over broad refactors.
• When fixing agent-run failures, trace to the failing tool call and its input or output before writing a fix. Validate against agent success rate and dismissal rate.
• Always include rollback steps and post-deploy metric monitoring. Validate changes against the expected metric impact before marking work complete.

Preferences that cross lanes

Put durable preferences that don't fit one lane cleanly, such as evidence rules or ownership constraints, in the lane they most affect. For example:

• Prioritize opportunities with evidence from at least two independent sources.
• Avoid opportunities that require changes outside the Checkout team unless they directly block checkout completion.

Avoid instructions such as:

• Investigate the drop from last Tuesday.
• Focus on the latest release.
• Find five bugs this week.

Run focused discovery

Product Area settings are durable, but not every investigation needs a settings change. Use a focused discovery run when you want the Opportunity Manager to investigate a specific overarching problem, user flow, page, or category of signal for one run. To start one, open the Product Area and choose Create > Generate opportunities.

Focused discovery is useful when you want to:

• Look for Opportunities around a specific page or flow, such as onboarding setup, checkout, or dashboard export.
• Direct agents toward a broad problem area, such as activation friction or performance issues.
• Investigate a category of signal, such as feedback themes, replay friction, web vitals, or experiment learnings.
• Go deeper into an area without changing the Product Area's durable scope, metrics, or instructions.

Focused discovery is different from submitting a manual Opportunity idea. Use focused discovery when you want agents to explore a scope and generate a batch of possible Opportunities. Use a manual Opportunity idea (Create > Create opportunity) when you already have a specific idea and want Amplitude to flesh it out with evidence, scoring, and a plan.

Feedback is the tuning loop

Feedback is the ongoing tuning mechanism within a Product Area. Amplitude uses both explicit feedback and workflow behavior to improve future Opportunity alignment for that Product Area.

Ratings

Click Rate this opportunity at the top of an Opportunity. Choose Positive when it's useful, well-scoped, and worth seeing more of. Choose Negative when the signal is wrong, the plan is weak, or it doesn't match the Product Area. Choose Neutral when it's neither.

Every labeled input gives Amplitude context about your team's preferences for that Product Area. That signal helps the next discovery cycle rank and write Opportunities that better match the Product Area's goals.

Direct feedback

Leave comments when an Opportunity is wrong, weak, duplicated, too broad, or missing important context. Good feedback explains the reason, not just the decision.

Examples:

• "This is out of scope for Checkout. It belongs to account billing settings."
• "The evidence is too replay-heavy. Need chart-backed volume before prioritizing."
• "This is a good quick win, but the proposed fix should avoid changing the payment provider."
• "Duplicate of the payment retry work already in progress."

Triage feedback

Your triage choices also teach the system:

• Moving an Opportunity to In progress signals that it matched the Product Area and the spec was actionable enough to start work.
• Shipped and its impact results close the loop from recommendation to outcome.

Don't leave low-quality or irrelevant opportunities in Triage forever. Rate them Negative and comment on why, then archive them. That gives the system stronger signal than ignoring them.

To review learned preferences or create reusable task instructions, go to Memories and skills.

Review cadence

For best results:

1. Review new opportunities after each discovery run.
2. Rate and archive irrelevant or weak opportunities instead of leaving them untouched.
3. Add comments when the reason matters for future discovery.
4. Move actionable opportunities through the lifecycle as work progresses.
5. Revisit Product Area settings when multiple opportunities show the same kind of mismatch.

Tuning isn't a one-time setup step. Treat Product Area settings like product strategy: the clearer the goals and boundaries, and the more honest the feedback, the more useful the recommendations become.