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. Also use when classification accuracy varies between runs or semantically close categories get confused., auto-categorize support tickets, AI labeling system, text classification with LLM, auto-tag content, email routing with AI, intent classification, sentiment analysis with DSPy, spam detection with AI, topic modeling with LLM, build a classifier without training data, zero-shot classification, AI triage system.
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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. Also use when classification accuracy varies between runs or semantically close categories get confused., auto-categorize support tickets, AI labeling system, text classification with LLM, auto-tag content, email routing with AI, intent classification, sentiment analysis with DSPy, spam detection with AI, topic modeling with LLM, build a classifier without training data, zero-shot classification, AI triage system.
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 importLiteral# 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"]
classSortContent(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"):
classTagContent(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:
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:
classSortWithContext(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
defload_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() andnot line.startswith("WEBVTT")
andnot re.match(r"\d{2}:\d{2}", line)
andnot 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
defload_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:
defload_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):
defvtt_to_segments(path):
"""Parse VTT into individual segments for per-segment sorting."""import webvtt # pip install webvtt-pyreturn [
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 ifnot 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:
If certain examples are ambiguous ("I want to cancel" — is that billing or account?), add a hint field that's only present during training:
classSortWithHint(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:
classSortWithConfidence(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 reviewprint(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
