Layered context injection for AI answers
Feed the model the user's current page state, schema, and account context before it answers so the AI can act like part of the product instead of a detached chatbot.
Why this can grow a startup
Users do not experience your product as a blank prompt. They arrive from a page, a role, a dataset, and a business context. When the agent receives that context up front, it can produce answers that fit the real task instead of generic best guesses. This lowers the amount of clarification the user has to provide, makes outputs feel more native to the product, and gives the AI a real advantage over a general model tab.
Source-backed example
PostHog says Max AI receives current dashboard state, visible insights, filters, role, schema details, account tier, timezone, and retention context so requests like why signups dropped last week can be answered inside the product context instead of from a blank chat.
Source-reported evidence
Source: PostHog Newsletter · Browse PostHog Newsletter tactics
PostHog describes page state, schema, role, organization tier, timezone, and retention as standard context sent with Max AI requests.
Attributed to the cited source; GrowthDex has not independently verified it. Use this as context, not as a forecast, benchmark, or recommended target.
Evidence scope: claims and metrics are attributed to the linked source; GrowthDex has not independently replicated the reported result. Check the original context and validate fit before applying it. Read the evidence methodology.
Source discovered: May 26, 2026
When to use it
Use this when Product, Onboarding, AI Search is relevant to ai products, activation, ux and you can run a bounded test with a medium budget.
When not to use it
Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.
How to do it
- Choose one target segment and define the exact next action this tactic should prompt.
- Adapt the tactic to Product and Onboarding, using evidence from PostHog Newsletter as context—not as a promised result.
- Record a baseline for: Share of target users completing the product's first-value action. Set a guardrail that would make you pause the test.
- Run a narrow pilot and choose a test window that fits the normal decision or use cycle; do not scale before you have observed the full cycle.
- Compare the signal with its baseline. Scale only if the intended action improves without crossing the guardrail; otherwise revise one variable or stop and record what you learned.
Compare a stack for your situation
Compare source-backed tactics against your product, stage, budget and channel strengths, then choose one experiment to test.
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