Make Your AI Follow the Rules

Guide the user through defining and enforcing rules their AI must follow. The key insight: don't ask the AI to follow rules — program constraints that enforce them automatically.

The two types of rules

DSPy gives you two constraint primitives:

import dspy

# Hard rule — will retry up to max_backtrack_attempts times
dspy.Assert(
    condition,       # bool: does the output satisfy the rule?
    "error message"  # str: feedback to the LM on what went wrong
)

# Soft rule — nudges but doesn't block
dspy.Suggest(
    condition,
    "suggestion message"
)

Step 1: Identify your rules

Ask the user:

1. What rules does the AI break? (too long? wrong format? forbidden content? missing fields?)
2. Which rules are hard requirements vs nice-to-haves? (Assert vs Suggest)
3. What should happen when a rule is broken? (retry, flag for review, fail loudly)

Step 2: Content policy rules

Enforce what the AI can and cannot say.

class PolicyCheckedResponse(dspy.Module):
    def __init__(self):
        self.respond = dspy.ChainOfThought("question -> answer")

    def forward(self, question):
        result = self.respond(question=question)
        answer = result.answer

        # Hard rules — must comply
        dspy.Assert(
            len(answer.split()) <= 280,
            f"Response is {len(answer.split())} words. Must be under 280 words."
        )
        dspy.Assert(
            not any(word in answer.lower() for word in BLOCKED_WORDS),
            "Response contains blocked words. Remove them and regenerate."
        )
        dspy.Assert(
            "disclaimer" not in answer.lower(),
            "Do not include disclaimers. Answer directly."
        )

        # Soft rules — prefer but don't block
        dspy.Suggest(
            answer[0].isupper(),
            "Response should start with a capital letter."
        )
        dspy.Suggest(
            answer.endswith(".") or answer.endswith("!") or answer.endswith("?"),
            "Response should end with proper punctuation."
        )

        return result

BLOCKED_WORDS = ["competitor_name", "profanity1", "profanity2"]  # your list

Step 3: Format rules

Enforce output structure — valid JSON, required fields, correct types.

import json
from pydantic import BaseModel, Field
from typing import Literal

# Option A: Pydantic validation (automatic)
class QuizQuestion(BaseModel):
    question: str = Field(min_length=10)
    options: list[str] = Field(min_length=4, max_length=4)
    correct_answer: str
    difficulty: Literal["easy", "medium", "hard"]

class GenerateQuiz(dspy.Signature):
    """Generate a quiz question about the topic."""
    topic: str = dspy.InputField()
    quiz: QuizQuestion = dspy.OutputField()

# Option B: Assert-based validation (custom logic)
class QuizGenerator(dspy.Module):
    def __init__(self):
        self.generate = dspy.ChainOfThought(GenerateQuiz)

    def forward(self, topic):
        result = self.generate(topic=topic)
        quiz = result.quiz

        # Correct answer must be one of the options
        dspy.Assert(
            quiz.correct_answer in quiz.options,
            f"Correct answer '{quiz.correct_answer}' is not in options {quiz.options}. "
            "The correct answer must be one of the four options."
        )

        # Options must be unique
        dspy.Assert(
            len(set(quiz.options)) == 4,
            "All four options must be different from each other."
        )

        return result

Combine Pydantic (catches type/structure errors) with Assert (catches logic errors) for the strongest format enforcement.

Step 4: Business constraint rules

Translate business requirements into programmatic constraints.

class PricingResponse(dspy.Module):
    def __init__(self):
        self.respond = dspy.ChainOfThought("customer_question, pricing_docs -> answer")

    def forward(self, customer_question, pricing_docs):
        result = self.respond(
            customer_question=customer_question,
            pricing_docs=pricing_docs,
        )

        # Never mention competitor pricing
        dspy.Assert(
            not any(comp in result.answer.lower() for comp in COMPETITORS),
            "Do not mention competitor pricing. Focus only on our plans."
        )

        # Never offer unauthorized discounts
        dspy.Assert(
            "discount" not in result.answer.lower() or "authorized" in result.answer.lower(),
            "Do not offer discounts unless referencing an authorized promotion."
        )

        # Always include a CTA
        dspy.Suggest(
            any(cta in result.answer.lower() for cta in ["contact", "sign up", "learn more", "get started"]),
            "Include a call-to-action at the end of the response."
        )

        return result

COMPETITORS = ["competitor_a", "competitor_b"]

Step 5: How retry and backtracking works

When dspy.Assert fails, DSPy doesn't just retry blindly — it feeds the error message back to the LM:

Attempt 1: LM generates response → Assert fails ("Response is 350 words, must be under 280")
Attempt 2: LM retries with feedback → Assert fails ("Response contains blocked words")
Attempt 3: LM retries with feedback → Assert passes ✓

Key details:

• Error messages matter. They're the LM's self-correction instructions. Be specific: "Response is 350 words, must be under 280" is better than "too long."
• Default retries: 2. Set via max_backtrack_attempts on the module.
• Each retry sees all previous failures. The model gets a cumulative error log.
• Suggest never retries. It sends the feedback but continues regardless.

Step 6: Composing multiple rules

Stack rules by putting multiple Assert/Suggest calls in sequence. They're checked in order.

class TweetWriter(dspy.Module):
    def __init__(self):
        self.write = dspy.ChainOfThought("topic, key_facts -> tweet")

    def forward(self, topic, key_facts):
        result = self.write(topic=topic, key_facts=key_facts)
        tweet = result.tweet

        # Rule 1: Length limit (hard)
        dspy.Assert(
            len(tweet) <= 280,
            f"Tweet is {len(tweet)} chars. Must be ≤280."
        )

        # Rule 2: No hashtags (hard)
        dspy.Assert(
            "#" not in tweet,
            "No hashtags allowed. Remove all # symbols."
        )

        # Rule 3: Must include key fact (hard)
        dspy.Assert(
            any(fact.lower() in tweet.lower() for fact in key_facts),
            f"Tweet must mention at least one key fact: {key_facts}"
        )

        # Rule 4: Engaging tone (soft)
        dspy.Suggest(
            not tweet.startswith("Did you know"),
            "Avoid starting with 'Did you know' — be more creative."
        )

        # Rule 5: No emojis (soft)
        dspy.Suggest(
            not any(ord(c) > 127 for c in tweet),
            "Prefer text-only tweets without emojis."
        )

        return result

When rules conflict (e.g., "include all key facts" vs "stay under 280 chars"), put the harder constraint first so the model prioritizes it.

Step 7: Optimizing with rules

DSPy optimizers work with assertions. When you optimize a module that has Assert/Suggest:

• The optimizer sees assertion pass/fail rates as part of the metric
• Optimized prompts learn to satisfy constraints more often
• Result: fewer retries needed in production

def metric(example, pred, trace=None):
    # Your quality metric + assertion compliance
    correct = pred.answer == example.expected_answer
    return correct  # assertions are enforced separately during forward()

optimizer = dspy.MIPROv2(metric=metric, num_threads=4)
optimized = optimizer.compile(
    my_module,
    trainset=trainset,
    max_bootstrapped_demos=4,
    max_labeled_demos=4,
)

Key principles

• Assert for requirements, Suggest for preferences. Don't use Assert for style issues.
• Specific error messages. "350 words, must be under 280" beats "too long."
• Pydantic + Assert together. Pydantic catches structure, Assert catches logic.
• Order matters. Put hard constraints before soft ones.
• Optimize after adding rules. DSPy learns to comply, reducing runtime retries.

Additional resources

• Use /ai-checking-outputs for general output verification (safety, quality gates)
• Use /ai-stopping-hallucinations for grounding AI in facts and sources
• Use /ai-improving-accuracy to measure and improve quality after adding rules
• Use /ai-testing-safety to verify your rules hold up against adversarial users
• See examples.md for complete worked examples