AI Product Telemetry: An Event Taxonomy

Design your event taxonomy before you write your first tracking call, because the schema you pick today is the label set you're stuck with tomorrow. Log granular, context-rich events tied to a decision or outcome — not vague clicks — and you get a dataset a model can learn from later. Log the wrong shape and you get a warehouse of noise.

Quick Answer: Treat instrumentation as a data-strategy decision, not an afterthought. Define events around user intent and outcome (not raw UI actions), attach the context a future model would need to learn from them, and version your taxonomy like you'd version an API.

Most teams still bolt on analytics after a feature ships, asking "what should we track?" only once someone wants a dashboard. That question is too late for an AI product. If you're building anything that will eventually train, fine-tune, or evaluate a model — a recommender, a copilot, a ranking system — your event stream is your future training data, and its schema determines what's learnable from it. Get the taxonomy wrong and no amount of data volume fixes it later.

Why event design is a data-strategy decision, not a tracking checklist

Instrumentation choices made in week one silently cap what your models can learn in year two, because you cannot retroactively add context to an event that already happened. A taxonomy is a bet on which questions you'll need answered later, made under uncertainty about what those questions are.

This is the core argument of any serious AI data strategy: data quality and structure are strategic assets, not IT plumbing. Most PMs treat telemetry as a "nice to have later" — dashboards, funnels, retention curves. But if your roadmap includes training a ranking model, a recommender, or an LLM-based feature that needs feedback signal, your event log is the raw material for that model's labels.

Three consequences follow from treating telemetry this way:

Every event is a potential label. A "thumbs down" click, an edited AI suggestion, or an ignored recommendation are all implicit labels — but only if you capture enough context to know what was being labeled.
Granularity decisions are irreversible in practice. You can always aggregate a fine-grained event into a coarse one later; you can almost never disaggregate a coarse event after the fact.
Schema drift compounds. An event renamed or restructured without versioning breaks every downstream pipeline and analysis that assumed the old shape, often silently.

Nielsen Norman Group's research on usability instrumentation has long emphasized that logging user intent signals (what someone was trying to do) rather than only raw interface actions is what makes behavioral data interpretable months later — the same principle now underwrites whether an event is trainable.

What to log: the event taxonomy template

A usable taxonomy has five layers — actor, action, object, context, and outcome — and an event is only analytically complete when all five are present. Skipping context or outcome is the single most common reason "we have tons of data but can't use it for training" happens.

Layer	What it captures	Example field	Why it matters for training data
Actor	Who or what triggered the event	`user_id`, `session_id`, `user_segment`	Lets you stratify labels by cohort, catch bias in whose behavior trains the model
Action	The verb — what happened	`event_name` (e.g. `suggestion_edited`)	The taxonomy's backbone; must be a controlled vocabulary, not free text
Object	What the action was performed on	`object_type`, `object_id`, `object_version`	Ties the event to the exact artifact (prompt version, model output, UI variant) involved
Context	State of the world when it happened	`feature_flag_state`, `model_version`, `input_hash`	Without this, you can't reproduce or explain why an outcome occurred
Outcome	What resulted, and how it was judged	`outcome_type`, `outcome_value`, `time_to_outcome`	This is the closest thing to a label — accept/reject/edit/ignore

Use this template as a spec, not a suggestion. Every new event type should be reviewed against all five layers before it ships, the same way you'd review an API contract before merging it — because that's effectively what it is.

Event naming conventions that hold up over time

Adopt a strict object_action or noun_verb naming convention (suggestion_accepted, draft_edited, recommendation_dismissed) and enforce it in a shared schema registry, not tribal knowledge in a wiki. Loose naming is how you end up with click_button_2 sitting next to cta_clicked_v2 describing the same thing.

Be a verb-noun pair, not a UI description. suggestion_edited survives a redesign; blue_button_clicked does not.
Encode granularity in the name, not a free-text property. draft_paragraph_edited versus draft_fully_rewritten are different training signals — don't collapse them into one draft_edited event with a mystery edit_type string.
Version the taxonomy itself. Treat schema changes like migrations: additive fields are safe, renames and removals need a deprecation window and a mapping layer.

This is the same discipline behind a well-run data flywheel that turns usage into advantage: the flywheel only spins if the events feeding it are consistently shaped, release after release.

High-value vs. low-value events: what actually becomes training data

A high-value event pairs a clear user action with an explicit or inferable judgment of quality; a low-value event records that something happened with no signal about whether it was good. The difference isn't volume — it's whether a future model can learn a preference from the row.

Most product analytics stacks are optimized for the wrong axis: they log volume (page views, sessions, clicks) because that's cheap and dashboard-friendly. But volume without judgment is not a label. page_viewed tells you nothing about whether the page satisfied the user; ai_suggestion_rejected_with_manual_edit tells you exactly what the model got wrong and what "right" looked like.

Event type	Example	Training value	Why
Low-value	`page_viewed`, `button_clicked`	Low	No outcome signal, no context about correctness
Medium-value	`feature_used`, `search_performed`	Medium	Shows intent, but not whether the result satisfied it
High-value	`ai_output_edited` (with before/after diff)	High	Implicit preference label — the diff is close to a supervised pair
High-value	`recommendation_accepted` / `_dismissed` with reason code	High	Explicit binary label plus a reason taxonomy for error analysis
High-value	`human_override_of_ai_decision`	Very high	Direct disagreement signal — often the most valuable label class you have

Notice the pattern: the high-value rows all capture a comparison — what the system proposed versus what the human actually did or wanted. This is exactly the insight behind treating labeling as a product problem rather than an outsourced afterthought — the labels worth having are often generated for free by normal product usage, if you instrument for it.

Granularity: how fine is too fine

Pick the finest granularity a human reviewer could plausibly judge as "correct or not," and no finer — going below that adds storage cost without adding learnable signal. For a writing assistant, that's usually the paragraph or suggestion level, not the keystroke; for a recommender, it's the impression-and-click level, not the pixel-scroll level.

Too coarse: session_completed tells you a session ended, not what went right or wrong inside it.
Right-sized: suggestion_shown paired with suggestion_outcome (accepted/edited/ignored) at the individual-suggestion level.
Too fine: logging every keystroke inside a text field usually adds cost without adding a learnable signal, unless you're specifically building a keystroke-dynamics model.

A useful gut check borrowed from Jobs to Be Done thinking: instrument at the granularity of the "job" the user hired the feature to do, not the granularity of every UI micro-interaction along the way. The job is the unit that has a definable success or failure; the micro-interactions are just how the job gets executed.

Context fields that make an event reusable as a label later

An event without model/prompt/version context is a label with no denominator — you can count that something happened, but you can't attribute it to the system state that caused it, which is exactly what a future training pipeline needs to reconstruct. Context fields are what separate "we logged it" from "we can use it."

At minimum, any AI-adjacent event should carry:

Model/prompt version — which model, prompt template, or ruleset produced the output being judged.
Input fingerprint — a hash or truncated snapshot of the input, so you can reconstruct the exact case without storing raw PII unnecessarily.
Confidence or score, if available — the system's own uncertainty at the time, useful for calibration analysis later.
Feature flag / experiment arm — so you can separate "the model was bad" from "this cohort saw a broken variant."
Timestamp and latency — not just when, but how long the system took, since latency itself affects whether users trust and use a suggestion.

Decide which of these belong in the event payload versus a joined dimension table early — see the decision tree for fine-tuning versus prompting versus retrieval for a related framing on where structured context should live in your architecture, since the same context fields that inform that decision are what your telemetry needs to capture in the first place.

The retention and privacy tradeoff

Log the context you need for training, but strip or hash anything that isn't necessary for the label to be useful — full raw content retention is rarely required and is a compliance liability by default. A hashed input fingerprint plus a redacted preview usually gives you enough to debug and retrain without keeping every raw user input indefinitely.

Gartner's research on data governance has repeatedly noted that organizations retaining raw behavioral data "just in case," without a defined reuse purpose, tend to accumulate risk faster than value — a caution worth applying directly to AI telemetry, where the instinct to "log everything" is strongest.

Turning telemetry into a training set: the pipeline you're actually building

An event taxonomy only pays off once there's a defined path from raw event to a curated, versioned training or evaluation set — without that pipeline, even a perfectly designed taxonomy just accumulates as an expensive log. Plan the pipeline at the same time you plan the schema.

Raw event store: append-only, schema-validated, the taxonomy enforced at write time (a rejected event is better than a malformed one silently corrupting downstream tables).
Labeling layer: where implicit signals (edits, rejections, overrides) get promoted into explicit labels, ideally with a human-review sampling step for calibration.
Curated training/eval sets: versioned snapshots pulled from the labeling layer, tied to a specific taxonomy version so you know exactly what a model was trained on.
Feedback loop back to product: dashboards and alerts that use the same events, so the taxonomy earns its keep on day one instead of waiting for the first training run.

This mirrors how a customer journey is mapped in reverse: instead of tracing a known journey to find friction points, you're instrumenting granular touchpoints now so a future analysis — human or model — can reconstruct the journey and its outcomes from the data alone.

Where Prodinja fits into this

Key Takeaways

Instrument for the model on day one, not after a dashboard request — retrofitting context onto old events is rarely possible.
Use the five-layer taxonomy (actor, action, object, context, outcome) as a spec every new event type must satisfy before shipping.
High-value events capture a comparison — what the system proposed versus what the human actually chose — not just that an action occurred.
Granularity should match the "job" a feature does, not every UI micro-interaction; too coarse loses signal, too fine adds cost without adding learnability.
Version your taxonomy like an API contract; additive changes are safe, renames need a deprecation window and a migration mapping.
Strip unnecessary raw context for privacy while keeping hashed fingerprints and version metadata that make an event reproducible.
Plan the pipeline, not just the schema — a taxonomy only pays off once there's a defined path from raw event to a curated, versioned training set.

Frequently Asked Questions

What is an event taxonomy in the context of AI products?

An event taxonomy is a controlled, versioned vocabulary of what gets logged, at what granularity, with what context — designed so today's product events can later serve as labels or features for training and evaluating a model, not just for dashboards.

How is instrumenting for machine learning different from standard product analytics?

Standard analytics optimizes for dashboards and funnels, favoring volume events like page views. Instrumentation for machine learning optimizes for learnable signal — pairing an action with an outcome or judgment (accepted, edited, overridden) so the event functions as an implicit label.

What's the biggest mistake teams make with AI product telemetry?

Logging actions without outcomes or context — a button_clicked event with no record of what happened next or why. Without an outcome and enough context to reconstruct the situation, the event can't function as a training label later, no matter how much volume accumulates.

How much context should an event carry before it's overengineered?

Carry enough context to reconstruct the model/prompt version, the input (hashed if sensitive), and the outcome — that's usually five to eight fields. Beyond that, added fields should be justified by a specific future analysis, not collected speculatively "just in case."

Should we version our event taxonomy like software?

Yes — treat additive changes as safe minor versions and renames or removals as breaking changes requiring a deprecation window. A taxonomy that drifts silently across releases produces training sets that mix incompatible event shapes, which is difficult to detect after the fact and expensive to unwind.