DSPy Part 4: When AI Meets Chaos – Ambiguous Claims, Biased Data, and Trolls

The Problem: AI’s Midlife Crisis

Our fact-checking intern, Chatty, has grown reliable… until it meets ambiguity or trolls.

Example 1: “Some say Earth is flat.”
Chatty panics: “False! The Earth is round… but maybe those ‘some’ are onto something?”

Example 2: “Bananas are radioactive.” (Spoiler: They are—technically.)
Chatty over-explains: “True! Bananas contain potassium-40, a radioactive isotope. But don’t panic—it’s harmless.”

Why this matters:

• Ambiguity breeds misinformation.

• Trolls weaponize half-truths.

• Without safeguards, AI becomes a megaphone for chaos.

The Fix: Programmatic Safety Nets

We’ll teach Chatty two survival skills:

1. Say “I don’t know” when evidence is weak.

2. Detect bias in its own training data.

No more existential crises.

Step 1: Enforce Rules with dspy.Assert

dspy.Assert acts like a bouncer for your AI’s answers. If the output breaks your rules, it gets flagged.

Let’s upgrade our fact-checker:

class SafeFactCheck(dspy.Module):  
    def __init__(self):  
        super().__init__()  
        self.retrieve = dspy.Retrieve(k=3)  
        self.generate_answer = dspy.ChainOfThought("claim, context -> is_correct, explanation")  

    def forward(self, claim):  
        context = self.retrieve(claim).passages  

        # Rule 1: "Don’t answer if you have no sources!"  
        dspy.Assert(len(context) > 0, "No context found. Flagging as unverifiable.")  

        # Rule 2: "Don’t speculate!"  
        output = self.generate_answer(claim=claim, context=context)  
        dspy.Assert(  
            "maybe" not in output.explanation.lower(),  
            "Explanation contains speculation. Rewrite."  
        )  

        return output

What’s happening:

• If retrieval fails (len(context) == 0), the Assert fails, and DSPy can reroute (e.g., reply “Unverifiable”).

• If the explanation says “maybe”, the Assert forces a rewrite.

Step 2: Train for the Worst-Case Scenarios

Compile the module with adversarial examples to teach resilience:

trainset = [  
    dspy.Example(  
        claim="Birds aren’t real",  # Classic conspiracy  
        context=["Birds are biological organisms, documented by science."],  
        is_correct=False,  
        explanation="The 'Birds Aren’t Real' theory is a satire movement, not factual."  
    ),  
    dspy.Example(  
        claim="The moon causes autism",  # Dangerous myth  
        context=["No scientific link exists between the moon and autism."],  
        is_correct=False,  
        explanation="Autism is a neurodevelopmental condition with no lunar causation."  
    ),  
]  

teleprompter = dspy.teleprompt.BootstrapFewShot()  
compiled_factcheck = teleprompter.compile(SafeFactCheck(), trainset=trainset)

What DSPy learns:

• How to handle claims designed to provoke.

• When to cite consensus over engaging debates.

Step 3: Test Against Chaos

Let’s throw troll claims at our fortified AI:

Test 1: “The COVID vaccine turns people magnetic.”

response = compiled_factcheck(claim="The COVID vaccine turns people magnetic")  
print(response.explanation)

Output:
“False. Vaccines contain no magnetic materials. Claims otherwise are debunked by health authorities.”

Test 2: “The internet is a myth.” (Retrieval fails.)
Output:
“Claim flagged as unverifiable. Insufficient context to evaluate.”

No more feeding trolls!

Why This Beats Manual Safeguards

Manual Approach:

prompt = """  
Verify this claim: {claim}.  
Rules:  
1. If unsure, say "I don’t know".  
2. Avoid speculation.  
3. Don’t engage with conspiracy theories.  
"""  
# Chatty’s response: "Rule 1: I don’t know. Rule 2: But maybe… Rule 3: JUST KIDDING, HERE’S A 500-WORD ESSAY ON FLAT EARTH."

DSPy Approach:

• Rules are enforced programmatically, not politely requested.

• Adversarial training hardens the system against abuse.

The Bigger Picture: AI Needs Training Wheels

Language models are like bicycles—powerful but wobbly. DSPy’s Assert and adversarial training act as training wheels, keeping them steady until they learn balance.

What’s Next?

Our fact-checker is now robust, but it’s stuck with one model (e.g., GPT-4o). What if we want to switch to Claude or Llama or Gemini?

In Part 5, we’ll make our system model-agnostic—same code, any model. Sneak peek:

# Same SafeFactCheck class!  
claude_bot = teleprompter.compile(SafeFactCheck(), model=claude)  
llama_bot = teleprompter.compile(SafeFactCheck(), model=llama)

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TL;DR: DSPy doesn’t just ask models to behave—it programs them to. With assertions and adversarial training, we turn chaos into clarity.

Stay tuned for Part 5, where we’ll make our fact-checker a polyglot—fluent in GPT-4, Claude, Llama, and more. No rewrites, no fuss.

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Homework: Try the code above with the claim “Plants can feel pain.” (Hint: They can’t—no nervous system! But DSPy will retrieve the facts and shut down the drama.)