ai-sorting

Original：🇺🇸 English

Translated

Auto-sort, categorize, or label content using AI. Use when sorting tickets into categories, auto-tagging content, labeling emails, detecting sentiment, routing messages to the right team, triaging support requests, building a spam filter, intent detection, topic classification, or any task where text goes in and a category comes out.

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Sourcelebsral/dspy-programming-not-prompting-lms-skills

Added on2026-02-09

NPX Install

npx skill4agent add lebsral/dspy-programming-not-prompting-lms-skills ai-sorting

SKILL.md Content

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Build an AI Content Sorter

Guide the user through building an AI that sorts, tags, or categorizes content. Powered by DSPy classification — works with any label set.

Step 1: Define the sorting task

Ask the user:

What are you sorting? (tickets, emails, reviews, messages, etc.)
What are the categories? (list all labels/buckets)
One category per item, or multiple? (e.g., "priority" vs "all applicable tags")

Step 2: Build the sorter

Single category (most common)

python

import dspy
from typing import Literal

# Your categories
CATEGORIES = ["billing", "technical", "account", "feature_request", "general"]

class SortContent(dspy.Signature):
    """Sort the message into the correct category."""
    message: str = dspy.InputField(desc="The content to sort")
    category: Literal[tuple(CATEGORIES)] = dspy.OutputField(desc="The assigned category")

sorter = dspy.ChainOfThought(SortContent)

Using

Literal

locks the output to valid categories only — the AI can't hallucinate labels.

ChainOfThought

adds reasoning which improves accuracy over bare

Predict

.

Multiple tags

python

class TagContent(dspy.Signature):
    """Assign all applicable tags to the content."""
    message: str = dspy.InputField(desc="The content to tag")
    tags: list[Literal[tuple(CATEGORIES)]] = dspy.OutputField(desc="All applicable tags")

tagger = dspy.ChainOfThought(TagContent)

Step 3: Test the quality

python

from dspy.evaluate import Evaluate

def sorting_metric(example, prediction, trace=None):
    return prediction.category == example.category

evaluator = Evaluate(
    devset=devset,
    metric=sorting_metric,
    num_threads=4,
    display_progress=True,
    display_table=5,
)
score = evaluator(sorter)

For multi-tag, use F1 or Jaccard similarity instead of exact match.

Step 4: Improve accuracy

Start with

BootstrapFewShot

— fast and usually gives a solid boost:

python

optimizer = dspy.BootstrapFewShot(
    metric=sorting_metric,
    max_bootstrapped_demos=4,
)
optimized_sorter = optimizer.compile(sorter, trainset=trainset)

If accuracy still isn't good enough, upgrade to

MIPROv2

:

python

optimizer = dspy.MIPROv2(
    metric=sorting_metric,
    auto="medium",
)
optimized_sorter = optimizer.compile(sorter, trainset=trainset)

Step 5: Use it

python

result = optimized_sorter(message="I was charged twice on my credit card last month")
print(f"Category: {result.category}")
print(f"Reasoning: {result.reasoning}")

Key patterns

Use
Literal
types to lock outputs to valid categories
Use
ChainOfThought
over
```
Predict
```
— reasoning improves sorting accuracy

Include a
hint
field during training for tricky examples:

python

class SortWithHint(dspy.Signature):
    message: str = dspy.InputField()
    hint: str = dspy.InputField(desc="Optional hint for ambiguous cases")
    category: Literal[tuple(CATEGORIES)] = dspy.OutputField()

Set

hint

in training data, leave empty at inference time.

Confidence scores: Add a float output field if you need confidence

Additional resources

For worked examples (sentiment, intent, topics), see examples.md
Need scores instead of categories? Use
```
/ai-scoring
```
Next:
```
/ai-improving-accuracy
```
to measure and improve your AI

ai-sorting

NPX Install

Tags

SKILL.md Content

Build an AI Content Sorter

Step 1: Define the sorting task

Step 2: Build the sorter

Single category (most common)

Multiple tags

Step 3: Test the quality

Step 4: Improve accuracy

Step 5: Use it

Key patterns

Additional resources