AI Generating Notifications

Build an AI Notification Generator

Guide the user through building AI that turns structured events into useful, channel-appropriate notification messages. Uses DSPy to produce consistent, personalized notification copy with urgency calibration and digest aggregation.

Step 1: Understand the notification task

Ask the user:

1. What events trigger notifications? (system alerts, user activity, scheduled digests, thresholds crossed?)
2. What channels do you target? (push/iOS/Android, email, Slack, SMS?)
3. Do you need personalization? (user name, role, preferences, history?)
4. Real-time or digest? (one notification per event, or aggregate multiple events into one message?)

Step 2: Build a single-event notifier

Basic signature

import dspy
from typing import Literal

lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

class GenerateNotification(dspy.Signature):
    """Write a clear, concise notification message for the target channel and recipient."""
    event: str = dspy.InputField(desc="Structured event data or description")
    recipient_profile: str = dspy.InputField(desc="Who receives the notification - role, name, preferences")
    channel: Literal["push", "email", "slack", "sms"] = dspy.InputField(desc="Delivery channel")
    notification_text: str = dspy.OutputField(desc="The notification message, respecting channel length limits")
    urgency_level: Literal["low", "medium", "high", "critical"] = dspy.OutputField(
        desc="Urgency level - low=informational, medium=needs attention, high=act soon, critical=act now"
    )

notifier = dspy.ChainOfThought(GenerateNotification)

result = notifier(
    event="User account login from new device - IP 203.0.113.42, Berlin, Germany",
    recipient_profile="Account owner, security-conscious, email preferred",
    channel="email",
)
print(result.notification_text)
print(result.urgency_level)

Step 3: Channel-specific constraints

Each channel has hard limits. Define them explicitly and enforce with a reward function.

CHANNEL_LIMITS = {
    "push": 150,    # title + body combined
    "email": 500,   # subject + preview text
    "slack": 500,
    "sms": 160,
}

def channel_length_reward(args, pred):
    """Hard penalty for exceeding channel length limits."""
    limit = CHANNEL_LIMITS.get(args["channel"], 300)
    text_len = len(pred.notification_text)
    if text_len <= limit:
        return 1.0
    # Hard fail above 2x limit, graduated penalty between limit and 2x
    if text_len > limit * 2:
        return 0.0
    return max(0.0, 1.0 - (text_len - limit) / limit)

notifier_enforced = dspy.Refine(
    module=dspy.ChainOfThought(GenerateNotification),
    N=3,
    reward_fn=channel_length_reward,
    threshold=0.9,
)

Step 4: Digest aggregation

Group multiple events into a single summary notification — reduces alert fatigue.

from pydantic import BaseModel, Field

class DigestOutput(BaseModel):
    subject: str = Field(description="Email subject line, max 60 chars")
    headline: str = Field(description="One-sentence summary of the most important event")
    event_groups: list[str] = Field(description="Events grouped by type, e.g. '3 new comments, 2 deployments'")
    call_to_action: str = Field(description="What the user should do next, if anything")

class GenerateDigest(dspy.Signature):
    """Aggregate multiple events into a single digest notification. Group similar events, highlight the most important, and keep it scannable."""
    events: list[str] = dspy.InputField(desc="List of events to include in the digest")
    recipient_profile: str = dspy.InputField(desc="Who receives the digest")
    time_period: str = dspy.InputField(desc="Time window covered - e.g. 'last 24 hours', 'this week'")
    digest: DigestOutput = dspy.OutputField()

class DigestNotifier(dspy.Module):
    def __init__(self):
        self.group = dspy.ChainOfThought("events -> grouped_events: list[str]")
        self.write = dspy.ChainOfThought(GenerateDigest)

    def forward(self, events, recipient_profile, time_period):
        # Group similar events first, then write the digest
        grouped = self.group(events=events).grouped_events
        return self.write(
            events=grouped,
            recipient_profile=recipient_profile,
            time_period=time_period,
        )

Step 5: Urgency calibration

Prevent over-alerting by calibrating urgency against event severity and recipient fatigue.

class CalibrateUrgency(dspy.Signature):
    """Assess the urgency of this event for this recipient. Consider event severity, recipient role, and whether action is required."""
    event: str = dspy.InputField(desc="Event description")
    recipient_profile: str = dspy.InputField(desc="Recipient role and preferences")
    recent_notification_count: int = dspy.InputField(
        desc="Number of notifications sent to this recipient in the last hour"
    )
    urgency_level: Literal["low", "medium", "high", "critical"] = dspy.OutputField()
    should_send: bool = dspy.OutputField(
        desc="False if recipient is already overloaded with high-urgency alerts"
    )
    rationale: str = dspy.OutputField(desc="One sentence explaining the urgency decision")

def urgency_reward(args, pred):
    """Penalize assigning high/critical urgency to clearly informational events."""
    score = 1.0
    informational_keywords = ["viewed", "logged in", "updated preferences", "exported"]
    event_lower = args["event"].lower()
    if any(kw in event_lower for kw in informational_keywords):
        if pred.urgency_level in ("high", "critical"):
            score -= 0.5  # soft: informational events should not be urgent
    return score

urgency_calibrator = dspy.Refine(
    module=dspy.ChainOfThought(CalibrateUrgency),
    N=3,
    reward_fn=urgency_reward,
    threshold=0.8,
)

Step 6: Personalization

Recipient context should influence tone, detail level, and channel preference.

class PersonalizedNotification(dspy.Signature):
    """Write a notification tailored to the recipient. Match tone to their role, include relevant context, and use their preferred channel style."""
    event: str = dspy.InputField(desc="Structured event data")
    recipient_name: str = dspy.InputField(desc="Recipient's name")
    recipient_role: str = dspy.InputField(desc="e.g. 'developer', 'executive', 'end user'")
    recipient_preferences: str = dspy.InputField(
        desc="e.g. 'brief and technical', 'plain language', 'include numbers'"
    )
    channel: Literal["push", "email", "slack", "sms"] = dspy.InputField()
    notification_text: str = dspy.OutputField()
    urgency_level: Literal["low", "medium", "high", "critical"] = dspy.OutputField()

Tone by role example:

ROLE_HINTS = {
    "developer": "technical details, stack traces welcome, use markdown in Slack",
    "executive": "business impact only, no jargon, one sentence if possible",
    "end_user": "plain language, friendly tone, tell them exactly what to do",
    "on_call": "all relevant details, include timestamp, severity, and system affected",
}

Step 7: Evaluate and optimize

Notification quality metric

class JudgeNotification(dspy.Signature):
    """Judge the quality of a notification message on clarity, actionability, and channel fit."""
    event: str = dspy.InputField(desc="Original event that triggered the notification")
    channel: str = dspy.InputField()
    notification_text: str = dspy.InputField()
    urgency_level: str = dspy.InputField()
    clarity: float = dspy.OutputField(desc="0.0-1.0 - is the message immediately understandable?")
    actionability: float = dspy.OutputField(desc="0.0-1.0 - does the recipient know what to do?")
    channel_fit: float = dspy.OutputField(desc="0.0-1.0 - is length and format right for the channel?")

def notification_metric(example, prediction, trace=None):
    judge = dspy.Predict(JudgeNotification)
    result = judge(
        event=example.event,
        channel=example.channel,
        notification_text=prediction.notification_text,
        urgency_level=prediction.urgency_level,
    )
    return (result.clarity + result.actionability + result.channel_fit) / 3

optimizer = dspy.BootstrapFewShot(metric=notification_metric, max_bootstrapped_demos=4)
optimized = optimizer.compile(notifier, trainset=trainset)
# Typical improvement: baseline ~0.61 avg score → ~0.83 after BootstrapFewShot with 4 demos
# Channel-fit jumps most (length compliance); actionability gains ~15-20 points

When NOT to use AI notifications

• Transactional messages (order confirmations, password resets, receipt emails) — use templates. The text must be exact and predictable; AI adds variability without value.
• Regulatory or compliance messages (GDPR notices, financial disclosures, legal alerts) — wording is fixed by requirement; AI-generated copy introduces compliance risk.
• Simple threshold alerts ("CPU > 90%", "balance below $10") — a format string is faster, cheaper, and more reliable than an LM call.

Key patterns

Gotchas

• Claude generates text that exceeds channel limits. Passing max_chars=160 in a field description is not enough — the model treats it as a suggestion. Always wrap with dspy.Refine and a programmatic length check reward function that reads len(pred.notification_text).
• Claude treats all events as equally urgent. Without explicit calibration, routine events ("user viewed a file") get marked high urgency. Add a CalibrateUrgency step and a reward function that penalizes over-classification of low-severity events.
• Claude uses dspy.Assert/dspy.Suggest for constraints. Use dspy.Refine with a reward function instead — it handles retries with feedback and is the current DSPy pattern for enforcing output constraints.
• Claude generates generic notifications that ignore recipient context. Without recipient_profile in the signature, every user gets the same message. Always pass name, role, and preferences as inputs to get personalized copy.
• Claude creates digests by listing events sequentially instead of grouping. "3 events happened: X, Y, Z" is not a digest — it is a log. Build a separate GroupEvents step before the notification writer to cluster similar events and count them before writing copy.

Cross-references

Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>

• Aggregate events intelligently before notifying — see /ai-summarizing
• Parse structured event payloads (JSON, logs) before feeding to notifier — see /ai-parsing-data
• Score notification quality automatically — see /ai-scoring
• Enforce output constraints with retry loops — see /dspy-refine
• Sample multiple notification variants and pick the best — see /dspy-best-of-n
• Write DSPy signatures for input/output contracts — see /dspy-modules
• Install /ai-do if you do not have it — it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do

Additional resources

• For worked examples (push notifications, weekly digest, incident Slack alerts), see examples.md
• For DSPy API signatures, parameter tables, and reward function patterns, see reference.md