Amplitude recently (re)introduced AI Chat as a way to help users use the platform more effectively. For a product as broad as Amplitude, this is not just a compelling idea – it’s a must-have. I’ve long dreamed about having an AI assistant to help me with the repetitive parts of Amplitude projects and even tried creating one myself. The platform has product analytics at its core, along with experimentation, data governance/CDP-like features, marketing attribution, B2B analytics, agent analytics, session replay, guides, surveys, AI Assistant, and an ever-growing list of many other features. A good AI partner inside the product could meaningfully reduce friction for both new and experienced users.
Being an Amplitude buff myself, I wanted to understand how far that promise goes today (and how close it is to actually doing my job). So I tested Amplitude AI Chat against a set of real-world Amplitude questions based on the kinds of questions people – especially new users – actually run into: implementation planning, tracking plan design, dashboard creation, debugging missing data, user identity disruption, group analytics, integrations, backfills, UI configuration, and various other topics. These were not just generic “what is a funnel?” questions. They were practical questions that often require a mix of strong product knowledge, implementation experience, and consulting judgment.
The result? I was very impressed with the new iteration of the AI Chat. But while the result was encouraging, it was pretty nuanced. Amplitude AI Chat is already useful, especially for standard platform questions and first-pass analysis support. It can save time, explain many concepts clearly, and help users move faster from a business question to a chart or dashboard. But it is not yet reliable enough to replace expert judgment in implementation-heavy, ambiguous, or high-risk scenarios.
Just to be clear: I’m only covering the AI Chat feature in this article. I’m leaving Amplitude’s AI agents and other AI features for a future post.
The headline results
Across the test set, I reviewed around 100 Amplitude questions. Roughly 55% of them were solved, 36% were partially useful, and 9% were not solved.
That is a respectable result for a product-specific AI assistant, especially given the range of questions tested and the depth of Amplitude’s platform. More than half of the answers were good enough to be considered solved, and many of the partial answers still gave useful direction. But the aggregate result hides an important pattern: performance varied significantly depending on question type and difficulty.
When grouped by difficulty, the pattern becomes clearer: the Amplitude AI Chat performed best on bounded, well-documented questions. It was much less consistent when the question required diagnosis, trade-off analysis, or practical knowledge of how Amplitude behaves in messy real implementations. That distinction matters because many Amplitude problems are not purely feature-usage questions. In real scenarios, the difficult questions are usually about data trust, user identity mapping, governance, migration strategy, source-of-truth decisions, and whether a chart is actually answering the business question correctly.
Where Amplitude AI Chat works well
The strongest answers came from questions where the domain was clear, and the expected answer was relatively well-defined. For example, it handled questions about A/B test data structure, the difference between “setGroup” and “groupIdentify”, importing CRM data for B2B use cases, setting properties to null, targeting Resource Center content with group properties, and interpreting retention behavior. In these cases, the AI Chat was often able to explain the concept, point me in the right direction, and respond reasonably well to follow-up questions.
It also showed real promise in chart and dashboard creation. In one real-world test, I asked it to build a dashboard covering DAU, MAU, release impact on feature usage, feature engagement, user conversion and journeys, retention, the relationship between feature usage and daily activity, and daily error counts. These are all fairly common starting points for most teams. The output still needed small tweaks, but that could’ve been solved with better prompting on my part, and it was strong enough to act as a meaningful first draft. That is probably one of the most valuable use cases for AI inside Amplitude: helping users translate a business question into an analytical starting point.
Many teams do not struggle with Amplitude because they lack access to data (this has gotten significantly easier over time). They struggle because they are unsure which chart type to use (Amplitude has many), which events to include, how to structure the comparison, or how to interpret the result. In that context, the AI Chat can reduce the blank-page problem. It can help users get from “I want to understand activation” to a reasonable analysis path faster than searching the documentation manually or asking a support engineer.
Where the answers became only partially useful
The most common issue was not complete failure; it was partial usefulness. The AI often provided a plausible answer, but missed a key caveat, a better recommendation developed through years of experience, or a common root cause.
For example, when asked why a chart was not showing data, it eventually suspected that the relevant event might be broken, but only later in the troubleshooting flow. When asked why many users had only one or two events, it gave a mostly reasonable explanation, but missed identity issues, which are a common cause in Amplitude implementations. When asked about backend data collection, it leaned heavily toward API-based options and did not consistently surface warehouse or cloud storage ingestion as alternatives (which can be much easier in most cases). For backfilling strategy, it recommended using the Batch API directly even when that might not be the most practical or thoughtful answer. What if I already have that data in cloud storage or a data warehouse? The AI seemed unaware that these import options existed.
This is where the difference between product knowledge and implementation judgment becomes visible. A feature-level answer might say, “Here are the ways you can send data to Amplitude.” A consultant-level answer would say, “Given your backend architecture, historical data volume, source of truth, production project state, and governance requirements, this is the safest ingestion path, and these are the mistakes to avoid.”
Amplitude AI Chat is often good at the first version. It is not yet consistently good at the second.
The biggest weakness: troubleshooting logic
The weakest area was systematic troubleshooting. This showed up across questions about missing users, inaccurate new user numbers, discrepancies between Amplitude and Google Analytics, empty property fields, Resource Center visibility, group property behavior, CSV import behavior, and historical user ID correction.
In several of these cases, the AI produced a list of possible causes. That can be useful, but lists are not the same as a diagnosis. Strong troubleshooting requires root-cause analysis. For example, for missing data, you would follow roughly this logical sequence: confirm whether the event fires, inspect SDK initialization, verify the network requests, validate the payload, check user consent behavior, confirm ingestion, inspect data filters and transformations, account for identity rules, and only then evaluate the chart configuration. The AI sometimes covered parts of this chain, but it did not reliably work through the full pipeline.
This is one area where Amplitude can likely improve the product itself, not just wait for better underlying AI models. Troubleshooting inside Amplitude has recognizable patterns. A well-designed AI assistant could be trained or orchestrated to follow structured diagnostic playbooks for common issues: missing data, broken events, identity mismatches, unexpected new user counts, data discrepancies, chart configuration mistakes, consent-related data loss, and source/destination problems.
For example, when a user asks why some users are not being tracked, the AI should not simply list ten possible explanations. It should behave more like a support engineer or implementation consultant: first identify the data source, then ask whether the missing users are concentrated by browser, platform, geography, consent state, release version, SDK version, or acquisition channel. It should know which checks can be performed inside Amplitude and which require browser/network inspection or engineering logs. That kind of structured root cause analysis is product behavior that Amplitude can design, not just model intelligence that has to emerge from the underlying LLM.
Hallucinations and overconfidence are still a concern
The most concerning answers were not the ones where the AI said it could not help. The more problematic cases were the ones where it gave an answer confidently, then backed down after being challenged. In one Salesforce integration-related test, it gave an incorrect response, then changed direction after being contradicted by documentation. In another case involving how latitude and longitude are used for location properties, it appeared to answer from memory, resisted correction, and only later acknowledged the right behavior after repeated prompting.
There were also examples of broken help center links and overly definitive claims, including around data backfills and data deletion approaches. This matters because experienced Amplitude users can challenge an answer, test it, and cross-check it. Less experienced users may not know when to push back.
This is partly a product design issue and partly a model-quality issue. Amplitude can (and probably will) improve retrieval, citations, documentation grounding, confidence handling, and escalation paths. The AI chat does tend to admit when it’s out of its depth and route users toward Support when the issue looks like a product bug, undocumented behavior, or account-specific configuration problem.
However, some of the remaining limitations depend on the underlying third-party AI model. General reasoning quality, consistency across long conversations, resistance to hallucination, and the ability to recover from incorrect assumptions may not be entirely under Amplitude’s control. Amplitude can constrain, ground, and guide the model, but it cannot fully eliminate the broader weaknesses of current LLMs. That distinction is important when evaluating the product fairly. I do expect this to continue to improve a lot over time, given the current pace of LLM advancement.
The AI is helpful, but not always consultative
Another theme from my testing was that AI Chat often answered the literal question, but did not always behave consultatively. For example, when asked about the components of a North Star Metric, it listed relevant concepts but did not guide the user toward a stronger framing. A consultant would usually ask about the product type, the value exchange, the usage frequency, and the customer lifecycle, among other things. However, the AI Chat did direct me to the correct feature to help me build my metric inside Amplitude.
The same pattern appeared in data requirements and use-case questions. The AI would suggest what data might be needed, but sometimes added unnecessary requirements without clearly marking them as optional. It could answer whether something was possible, but did not always explain whether it was advisable. Any analytics consultant worth their salt will tell you it’s not recommended to inflate your tracking plan with events and properties that are a second priority; quickly implementing a lean tracking plan is, in most cases, more advisable than spending too much time on an exhaustive data taxonomy.
In Amplitude, that distinction matters. The “correct” answer is often not enough. Teams need to know whether an approach is maintainable, whether it will create governance issues, whether it will confuse business users, whether it scales across teams, and whether the analysis is worth building in the first place. AI Chat can help with the mechanics of Amplitude usage, but the higher-value work is still deciding what should be implemented, how it should be structured, and how it will be adopted.
How Amplitude AI Chat can improve
In my opinion, there are two key improvement paths for the Amplitude AI Chat.
The first is Amplitude-specific product improvement. This includes better documentation retrieval, stronger grounding in official product behavior, structured diagnostic workflows for common troubleshooting scenarios, and, when all resolution paths are exhausted, a clearer escalation to Support. Amplitude has a major advantage here because many user questions occur inside a known product context. The AI already inspects chart configuration and comes “pre-trained” on your data and content, but in the future, it should recognize data-source patterns and guide users through Amplitude-specific checks in a predictable sequence.
This is where I would expect the biggest near-term gains. For example, troubleshooting missing data, inaccurate user counts, identity issues, SDK setup problems, and chart misconfiguration could become much more reliable if the AI followed explicit diagnostic frameworks. The assistant should know when to ask for more context, when to inspect available project data, when to suggest a test event, when to check filters, when to consider consent, and when to stop speculating and recommend Support. ChatGPT already does this pretty well and, in some cases, offers better answers than the Amplitude AI Chat.
The second improvement path depends on the underlying AI model. Some weaknesses are broader LLM issues: inconsistency, overconfidence, long-context drift, incomplete reasoning, and occasional hallucination. Amplitude can reduce these problems through better grounding and product design, but it cannot fully solve them alone. If the model itself improves at reasoning, uncertainty handling, and multi-step technical diagnosis, Amplitude AI Chat will benefit. But until then, users should treat it as a strong assistant rather than an authority.
Where teams should use it today
I would recommend using Amplitude AI Chat for standard product questions, first-pass chart creation, dashboard ideation, chart explanations, basic implementation guidance, and exploratory analysis. It is especially useful when the cost of being slightly incomplete is low. If you are trying to understand a concept, generate an analysis starting point, or get unstuck on a common workflow, it can save time (as it did for me).
I would be more careful using it as the only source of truth for complex issues like user identity topics, historical data migration, backfills, governance decisions, cross-tool discrepancies, privacy-related remediation, and debugging missing or inaccurate data. These are areas where a wrong recommendation can create long-term consequences, since data in analytics tools is still hard to fix. A bad chart can be corrected quickly. A bad implementation pattern can pollute months or years of data and become very costly to fix.
Final verdict
Amplitude AI Chat is good enough to be genuinely useful, but not good enough to trust blindly. It performs well on many straightforward and moderately complex product questions, and it shows real promise as a chart-building and analysis-assistance layer inside Amplitude. For teams that are still learning the platform, it can reduce friction and accelerate day-to-day usage.
Its limitations become clear when questions require expert diagnosis, implementation strategy, or decision-making under ambiguity. It can miss common root causes, over-index on documented feature behavior, give incomplete recommendations, or answer too confidently when the pros and cons of a given solution should be weighed carefully.
The best way to think about Amplitude AI Chat today is as a capable junior product analytics assistant. It can speed up the easy (and repetitive) work, generate useful starting points, and help users learn the product faster. But for the decisions that shape the reliability, scalability, and long-term value of your Amplitude data, expert judgment still matters.
In that sense, AI Chat does not make Amplitude expertise less valuable. It changes where that expertise is needed. As AI handles more basic enablement and first-pass analysis, the remaining value shifts toward higher-leverage work: designing clean tracking plans, choosing robust user identity strategies, building scalable analytics, debugging data quality issues, creating governance processes, and helping teams turn product data into decisions they can actually trust.
