Build an AI Content Sorter

Build an AI sorter with DSPy: define categories, load data, evaluate, optimize, and deploy.

Step 1: Define the sorting task

Ask the user:

What are you sorting? (tickets, emails, reviews, messages, comments, etc.)
What are the categories? (list all labels/buckets)
One category per item, or multiple? (e.g., "priority" vs "all applicable tags")
Do you have labeled examples already? (a CSV, database, spreadsheet with items + their correct category)

The answers determine which pattern to use below.

When NOT to use AI sorting

Categories are deterministic — if you can write regex or keyword rules that cover 95%+ of cases, skip the LM. A message.contains("invoice") rule is faster, cheaper, and more predictable than an LM call.
You need exact reproducibility — LM outputs can vary between runs. If identical inputs must always produce identical outputs (e.g., for compliance), use rule-based logic or pin temperature=0 and accept minor model-version drift.
Binary filtering with clear signal — spam filters where a blocklist or Bayesian filter already works well do not need an LM.

Step 2: Build the sorter

Single category (most common)

import dspy
from typing import Literal

# Configure your LM — works with any provider
lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

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

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

sorter = dspy.ChainOfThought(SortContent)

Literal locks the output to valid categories — the model cannot invent labels.

| Module | When to use | Tradeoff | |--------|-------------|----------| | ChainOfThought | Default — most classification tasks | ~5-15% accuracy gain over Predict, but 2x tokens | | Predict | Binary/obvious categories (spam vs not-spam) | Faster and cheaper, skip if reasoning is not helping |

Multiple tags

When items can belong to several categories at once (e.g., a news article that's both "technology" and "business"):

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)

Handling "none of the above"

If real-world content might not fit any category, add an explicit catch-all rather than hoping the model picks the least-bad option:

CATEGORIES = ["billing", "technical", "account", "feature_request", "other"]

This gives the model a safe escape hatch and makes it easy to filter out uncategorized items for human review.

Sorting with context

Sometimes classification depends on extra context — a customer's plan tier, previous interactions, or business rules. Add those as input fields:

class SortWithContext(dspy.Signature):
    """Sort the ticket considering the customer's context."""
    message: str = dspy.InputField(desc="The support message")
    customer_tier: str = dspy.InputField(desc="Customer plan: free, pro, or enterprise")
    category: Literal[tuple(CATEGORIES)] = dspy.OutputField()
    priority: Literal["low", "medium", "high", "urgent"] = dspy.OutputField()

Step 3: Load your data

If the user has labeled data, help them load it. The key step is converting their data into dspy.Example objects and marking which fields are inputs (what the model sees) vs outputs (what it should predict).

From a CSV or DataFrame

import pandas as pd

df = pd.read_csv("labeled_tickets.csv")  # columns: message, category

dataset = [
    dspy.Example(message=row["message"], category=row["category"]).with_inputs("message")
    for _, row in df.iterrows()
]

# Split into train/dev sets
trainset, devset = dataset[:len(dataset)*4//5], dataset[len(dataset)*4//5:]

From a list of dicts

data = [
    {"message": "I was charged twice", "category": "billing"},
    {"message": "Can't log in", "category": "technical"},
    # ...
]

dataset = [dspy.Example(**d).with_inputs("message") for d in data]

From transcripts (VTT, LiveKit, Recall)

Transcripts are a common source for sorting — classifying call topics, tagging meeting segments, routing conversations. The key is extracting the text content from whatever format you have.

WebVTT (.vtt) files:

import re

def load_vtt(path):
    """Extract text lines from a VTT transcript, stripping timestamps."""
    text = open(path).read()
    # Remove VTT header and timestamp lines
    lines = [line.strip() for line in text.split("\n")
             if line.strip() and not line.startswith("WEBVTT")
             and not re.match(r"\d{2}:\d{2}", line)
             and not line.strip().isdigit()]
    return " ".join(lines)

# Sort entire transcripts by topic
transcript = load_vtt("meeting.vtt")
dataset = [dspy.Example(message=transcript, category="standup").with_inputs("message")]

LiveKit transcripts (from LiveKit Agents egress or webhook data):

import json

def load_livekit_transcript(path):
    """Extract text from a LiveKit transcript JSON export."""
    data = json.load(open(path))
    # LiveKit transcription segments have text + timestamps
    segments = data.get("segments", data.get("results", []))
    return " ".join(seg.get("text", "") for seg in segments)

transcript = load_livekit_transcript("call_transcript.json")

Recall.ai transcripts:

def load_recall_transcript(transcript_data):
    """Extract text from a Recall.ai transcript response.
    transcript_data is the JSON from Recall's /transcript endpoint."""
    return " ".join(
        entry["words"]
        for entry in transcript_data
        if entry.get("words")
    )

Sorting transcript segments — often you want to classify individual segments rather than whole transcripts (e.g., tag each speaker turn by topic):

def vtt_to_segments(path):
    """Parse VTT into individual segments for per-segment sorting."""
    import webvtt  # pip install webvtt-py
    return [
        dspy.Example(message=caption.text, category="").with_inputs("message")
        for caption in webvtt.read(path)
        if caption.text.strip()
    ]

From Langfuse traces

If you're sorting AI interactions logged in Langfuse — classifying traces by quality, topic, failure mode, etc.:

from langfuse import Langfuse

langfuse = Langfuse()

# Fetch traces to classify
traces = langfuse.fetch_traces(limit=200).data

dataset = [
    dspy.Example(
        message=trace.input.get("message", str(trace.input)),
        # If traces are already scored/tagged in Langfuse, use that as the label
        category=trace.tags[0] if trace.tags else ""
    ).with_inputs("message")
    for trace in traces
    if trace.input
]

# Filter out unlabeled ones for training, keep them for batch classification
labeled = [ex for ex in dataset if ex.category]
unlabeled = [ex for ex in dataset if not ex.category]

No labeled data yet

If the user doesn't have labeled examples, they have two options:

Label a small set by hand — even 20-30 examples helps. Suggest they pick representative examples from each category.
Use /ai-generating-data — generate synthetic training data from category descriptions.

Step 4: Evaluate quality

Before optimizing, measure how the baseline performs:

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,  # show 5 example results
)
score = evaluator(sorter)
print(f"Baseline accuracy: {score}%")

Multi-label metric

For multi-tag classification, exact match is too strict. Use Jaccard similarity (intersection over union):

def multilabel_metric(example, pred, trace=None):
    gold = set(example.tags)
    predicted = set(pred.tags)
    if not gold and not predicted:
        return 1.0
    return len(gold & predicted) / len(gold | predicted)

Step 5: Optimize accuracy

| Optimizer | When to use | What it optimizes | |-----------|-------------|-------------------| | BootstrapFewShot | Start here — fast, typically gives 10-20% accuracy bump | Selects few-shot demos from training data | | MIPROv2 | Accuracy plateaus after BootstrapFewShot | Demos + instructions jointly |

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

# Re-evaluate
score = evaluator(optimized_sorter)
print(f"Optimized accuracy: {score}%")

If accuracy plateaus, upgrade to MIPROv2:

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

Training hints for tricky examples

If certain examples are ambiguous ("I want to cancel" — is that billing or account?), add a hint field that's only present during training:

class SortWithHint(dspy.Signature):
    """Sort the message into the correct category."""
    message: str = dspy.InputField()
    hint: str = dspy.InputField(desc="Clarifying context for ambiguous cases")
    category: Literal[tuple(CATEGORIES)] = dspy.OutputField()

# In training data, provide hints
trainset = [
    dspy.Example(
        message="I want to cancel",
        hint="Customer is asking about canceling their subscription billing",
        category="billing"
    ).with_inputs("message", "hint"),
]
# At inference time, pass hint="" or omit it

Step 6: Use it

Single item

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

Batch processing

For sorting many items at once, use dspy.Evaluate with your data or a simple loop. The evaluator handles threading automatically:

# Quick batch with a loop
results = []
for item in items:
    result = optimized_sorter(message=item["text"])
    results.append({"text": item["text"], "category": result.category})

# Or use pandas
df["category"] = df["message"].apply(
    lambda msg: optimized_sorter(message=msg).category
)

Confidence-based routing

When you need to know how sure the model is — for example, to escalate low-confidence items to a human:

class SortWithConfidence(dspy.Signature):
    """Sort the content and rate your confidence."""
    message: str = dspy.InputField()
    category: Literal[tuple(CATEGORIES)] = dspy.OutputField()
    confidence: float = dspy.OutputField(desc="Confidence between 0.0 and 1.0")

sorter = dspy.ChainOfThought(SortWithConfidence)
result = sorter(message="I think there might be an issue")

if result.confidence < 0.7:
    # Flag for human review
    print(f"Low confidence ({result.confidence}) — needs human review")
else:
    print(f"Category: {result.category} (confidence: {result.confidence})")

Save and load

Persist your optimized sorter so you don't have to re-optimize every time:

# Save
optimized_sorter.save("ticket_sorter.json")

# Load later
sorter = dspy.ChainOfThought(SortContent)
sorter.load("ticket_sorter.json")

Gotchas

Using Literal[list] instead of Literal[tuple(list)]. Claude writes Literal[["a", "b"]] which raises a TypeError. Must be Literal[tuple(["a", "b"])] — Python requires a tuple of values inside Literal.
Categories > 15 degrade accuracy. With many flat categories, the LM confuses semantically close labels. Use hierarchical classification (coarse category first, then sub-category) instead of a flat list.
Omitting a catch-all category. Without "other" or "unknown", the model is forced to misclassify edge cases into the closest wrong bucket. Always include an explicit escape hatch for content that does not fit.
Using verbose category names like "Issues related to billing". Short, unambiguous names ("billing_issue") give the LM a clearer signal. Add a desc field on the signature only if the name alone is ambiguous.
Skipping adversarial inputs in the dev set. Inputs that span two categories or contain no relevant content expose classification weaknesses early. Add these before optimizing, not after.

Cross-references

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

Need scores instead of categories? See /ai-scoring
Measure and improve sorting accuracy — see /ai-improving-accuracy
Generate training data when you have none — see /ai-generating-data
Define input/output contracts for signatures — see /dspy-signatures
Add reasoning before classification — see /dspy-chain-of-thought
Simple classification without reasoning — see /dspy-predict
Constrain output quality with reward functions — see /dspy-refine
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 (sentiment, intent routing, topics, hierarchical), see examples.md
For DSPy API details (constructors, parameters, methods), see reference.md