The LLM Feature That “Felt Faster” (Until We Measured It and Found a 14% Conversion Drop)

The fastest way to lose credibility with leadership: “It feels better”

I’ve watched this movie at least a dozen times. A team ships an AI augmentation—auto-summaries in the CRM, a support copilot, an “intelligent” checkout assistant. Demos look great. The PM is excited. Someone says the magic words: “Users will love it.”

Then production happens. Latency creeps from 800ms to 6–8 seconds at P99 because the model’s having a bad day. Hallucinations slip into customer-facing answers. Drift shows up when the product catalog changes and RAG starts retrieving stale docs. And suddenly Finance wants to know why the OpenAI/Azure bill doubled while conversion quietly dropped.

Here’s what actually works: treat AI-enabled flows like any other revenue-critical system. Instrument first, add guardrails second, and only then ship via controlled experiments. GitPlumbers gets called when teams skip those steps and have to explain to the CEO why “the AI feature” is now a Sev-1.

Decide what “impact” means before you touch a prompt

If you don’t define impact up front, you’ll end up measuring whatever is easiest (token counts and thumbs-up emojis) instead of what matters.

Pick a primary KPI tied to the business:

• Checkout assistant → conversion rate, AOV, refund rate
• Support copilot → deflection rate, time-to-resolution, CSAT
• Sales email generator → meetings booked, reply rate, unsubscribe rate

Then add supporting metrics that keep you honest:

• Cost: cost_per_success, tokens per successful outcome, provider spend per 1k sessions
• Performance: P95/P99 latency, timeout rate, retry rate
• Reliability/quality: groundedness/citation rate, policy violation rate, escalation rate

A metric set that’s saved teams real pain:

• ai_success_rate (task-level, not “did it return text”)
• ai_guardrail_block_rate (how often safety catches something)
• ai_fallback_rate (how often you bail out to the non-AI path)
• ai_cost_per_success (the metric Finance will actually care about)

If you can’t compute “cost per successful outcome,” you’re not running a product—you’re running a science fair.

Instrumentation that doesn’t lie: traces + structured events + metrics

You need observability across the whole user journey, not just the LLM call. “The model was slow” is rarely the full story—tool calls, vector DB latency, rate limits, and retries are usually the culprits.

The baseline stack we see work in production

• OpenTelemetry for distributed tracing (trace_id everywhere)
• Prometheus for SLIs (latency histograms, error counters)
• Grafana for dashboards + alerting
• Structured logs (JSON) to your log pipeline (ELK, Datadog, Loki)
• Optional but helpful: Langfuse/Arize/WhyLabs for LLM-specific tracing, evals, and drift signals

A concrete TypeScript example (Express + OpenTelemetry)

Instrument the AI call like any other downstream dependency, and attach the metadata you’ll need later (prompt version, model, token usage, guardrail outcomes).

import express from "express";
import { trace, context, SpanStatusCode } from "@opentelemetry/api";

const app = express();
const tracer = trace.getTracer("ai-checkout", "1.0.0");

app.post("/api/checkout-assist", async (req, res) => {
  const span = tracer.startSpan("checkout_assist.request", {
    attributes: {
      "ai.feature": "checkout-assist",
      "ai.variant": req.header("x-exp-variant") ?? "unknown",
      "user.tier": req.header("x-user-tier") ?? "anon",
    },
  });

  try {
    const promptVersion = "checkout-assist@2025-12-01";

    const result = await context.with(trace.setSpan(context.active(), span), async () => {
      // Call your LLM wrapper here (OpenAI, Azure OpenAI, Bedrock, etc.)
      return await callLLM({
        model: "gpt-4.1-mini",
        promptVersion,
        input: req.body,
        timeoutMs: 4500,
      });
    });

    span.setAttributes({
      "ai.model": result.model,
      "ai.prompt_version": promptVersion,
      "ai.tokens.prompt": result.usage.prompt_tokens,
      "ai.tokens.completion": result.usage.completion_tokens,
      "ai.latency_ms": result.latencyMs,
      "ai.guardrail.blocked": result.guardrail?.blocked ?? false,
      "ai.tool.calls": result.toolCalls?.length ?? 0,
    });

    span.setStatus({ code: SpanStatusCode.OK });
    res.json({ output: result.output, fallback: result.fallback ?? false });
  } catch (err: any) {
    span.recordException(err);
    span.setStatus({ code: SpanStatusCode.ERROR, message: err.message });
    // IMPORTANT: return a safe fallback path
    res.status(200).json({ output: null, fallback: true });
  } finally {
    span.end();
  }
});

Prometheus metrics you can alert on

If you only ship one thing, ship a latency histogram and a task-level success counter.

import client from "prom-client";

export const aiLatency = new client.Histogram({
  name: "ai_request_duration_seconds",
  help: "AI request duration",
  labelNames: ["feature", "variant", "model", "fallback"],
  buckets: [0.25, 0.5, 1, 2, 3, 5, 8, 13],
});

export const aiSuccess = new client.Counter({
  name: "ai_task_success_total",
  help: "Count of successful AI task outcomes",
  labelNames: ["feature", "variant", "reason"],
});

And a Prometheus alert that catches the “LLM is melting down” moment before Twitter does:

groups:
  - name: ai-slos
    rules:
      - alert: AICheckoutAssistP99LatencyHigh
        expr: |
          histogram_quantile(0.99,
            sum(rate(ai_request_duration_seconds_bucket{feature="checkout-assist"}[5m])) by (le)
          ) > 5
        for: 10m
        labels:
          severity: page
        annotations:
          summary: "P99 AI latency > 5s for 10m"
          description: "Likely provider slowness, tool latency, or retry storm. Consider circuit breaker + fallback." 

Guardrails: the stuff you’ll wish you added after the first incident

When AI breaks, it doesn’t break like a normal service. It returns plausible garbage. It returns the wrong answer confidently. Or it times out in the worst possible place (checkout, incident response, account recovery).

The guardrails that actually reduce risk (not just make people feel safe):

• Strict output schemas: validate with zod/jsonschema and reject invalid structures
• Citations/grounding requirements for RAG answers (no citation → fallback)
• PII redaction + allowlists before sending to the model (and before logging)
• Timeouts + circuit breakers (timeoutMs, max retries, exponential backoff)
• Safe fallback path (non-AI baseline) with an explicit fallback=true signal
• Rate limiting and per-tenant quotas (prevents noisy neighbor + cost explosions)

Example: schema validation to stop “creative” outputs from breaking downstream systems.

import { z } from "zod";

const CheckoutAdvice = z.object({
  recommendedPaymentMethod: z.enum(["card", "paypal", "apple_pay"]),
  reason: z.string().max(240),
  upsell: z.object({ sku: z.string(), confidence: z.number().min(0).max(1) }).optional(),
});

export function parseAdvice(raw: unknown) {
  const parsed = CheckoutAdvice.safeParse(raw);
  if (!parsed.success) return { fallback: true };
  return { fallback: false, advice: parsed.data };
}

This is also where you instrument guardrail outcomes. If guardrail_block_rate jumps, that’s usually drift, a prompt regression, or a retrieval issue.

Controlled experiments: prove lift (or kill it quickly) without burning the business

I’ve seen teams “roll out gradually” and still learn nothing because they didn’t keep a clean control group. You need an experiment design that survives real traffic and real weirdness.

A rollout that doesn’t lie

1. Holdback first: Keep 5–10% of eligible traffic permanently on the non-AI path.
2. Ramp the variant: Start at 1%, then 5%, 25%, 50% while watching SLIs and cost.
3. Stop conditions: predefine thresholds (conversion down 2%? rollback; P99 > 5s? fallback-only).

A feature flag example (LaunchDarkly-style) that makes the experiment explicit:

featureFlags:
  checkoutAssist:
    key: "checkout-assist"
    variations:
      - name: "control"
        value: { enabled: false }
      - name: "ai_on"
        value: { enabled: true, model: "gpt-4.1-mini" }
    rules:
      - clauses:
          - attribute: "country"
            op: "in"
            values: ["US", "CA"]
        variation: "ai_on"
        rollout:
          kind: "experiment"
          weights:
            control: 10
            ai_on: 90

Tie experiment analysis to traces and outcomes

The trick is correlating experiment assignment with business events:

• Put the variant in a header (x-exp-variant) and propagate it.
• Log it with every purchase/support ticket outcome.
• Join in your warehouse.

Example SQL (BigQuery-ish) for lift and cost-per-success:

WITH sessions AS (
  SELECT
    session_id,
    variant,
    MAX(CASE WHEN event = 'purchase' THEN 1 ELSE 0 END) AS converted,
    SUM(ai_cost_usd) AS ai_cost
  FROM analytics.session_events
  WHERE event_date BETWEEN '2025-12-01' AND '2025-12-14'
    AND experiment_key = 'checkout-assist'
  GROUP BY 1,2
)
SELECT
  variant,
  COUNT(*) AS sessions,
  AVG(converted) AS conversion_rate,
  SUM(ai_cost) / NULLIF(SUM(converted), 0) AS cost_per_conversion
FROM sessions
GROUP BY 1;

If the AI variant improves conversion by 0.4% but adds $3.20 cost per conversion, you can finally have the grown-up conversation: is that worth it relative to CAC/LTV?

The failure modes you should assume will happen (and what to do about them)

Hallucination (aka “confidently wrong”)

What it looks like:

• Support bot invents a refund policy
• Sales copilot fabricates customer details
• RAG answers without actually retrieving anything relevant

Mitigations that hold up under pressure:

• Grounded generation: require citations to your KB/docs; no citation → fallback/escalate
• Tool-first for facts: call pricing_service, order_status_service, etc. Don’t “ask the model” for truth
• Red team prompts + regression tests for known failure patterns

Drift (aka “it worked last month”)

What it looks like:

• New SKUs and renamed fields break retrieval
• Customer language changes (seasonality, promotions)
• Prompt tweaks silently reduce quality

Mitigations:

• Version prompts like code (prompt_version) and alert on quality regressions by version
• Track embedding/retrieval health: top-k similarity distributions, “no result” rate
• Run a scheduled eval set nightly (even 50 curated examples catches a lot)

Latency spikes (aka “we DDOS’d ourselves with retries”)

What it looks like:

• Provider throttling (429s), retry storms
• Vector DB hot partitions
• Tool calls serialize and blow up P99

Mitigations:

• Hard timeouts and bounded retries
• Circuit breaker to force fallback when error/latency crosses thresholds
• Cache the boring stuff (static summaries, deterministic tool results)

Load test the AI path like it’s a payment provider. k6 makes this painfully obvious:

k6 run --vus 50 --duration 5m scripts/checkout_assist_loadtest.js

If P99 goes nonlinear at 30 VUs, you don’t have an AI feature—you have an outage generator.

The GitPlumbers playbook: ship AI like you want to keep your job

When GitPlumbers comes in for “vibe code cleanup” or an AI rollout that’s gone sideways, the fix is usually not “a better prompt.” It’s production discipline.

A practical sequence that works:

1. Instrument the full flow (OTel traces + structured events + Prometheus metrics). No debating this.
2. Define SLOs for AI endpoints: availability, P95/P99 latency, task success rate, cost-per-success.
3. Add guardrails: schema validation, citations, redaction, timeouts, circuit breaker, safe fallback.
4. Ship behind a feature flag with an explicit holdback group.
5. Run controlled experiments long enough to catch seasonality, then decide: ramp, iterate, or kill.

If you want a second set of eyes, GitPlumbers does this kind of work without the “enterprise transformation” theater—instrumentation, controlled rollouts, and safety guardrails that survive contact with real users.

• Related: AI Delivery & Production Hardening: /services/ai-delivery
• Related: Code Rescue for AI-Generated Code: /services/vibe-coding-help
• Case studies: /case-studies