Module 12 Wrap-up: The Reliability Architect

You have learned that "Guesstimating" is the enemy of production agents. We fight hallucinations through Structure, Validation, and Benchmarking. Now, you will combine these techniques to build a Validated Data Extractor.

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Hands-on Exercise: The Guaranteed Extractor

The Goal: Build an agent that extracts "Name" and "Age" from a messy text string. It must use a Pydantic schema, and it must loop back to the LLM if the extraction is invalid (e.g., if the Age is a negative number).

1. Requirements

pip install pydantic langchain

2. The Logic (Python)

from pydantic import BaseModel, Field, field_validator

# 1. Define the Schema with CUSTOM VALIDATION
class UserInfo(BaseModel):
    name: str = Field(description="The full name of the user")
    age: int = Field(description="The age in years")

    @field_validator('age')
    @classmethod
    def age_must_be_positive(cls, v: int) -> int:
        if v < 0:
            raise ValueError('Age cannot be negative!')
        return v

# 2. The Agent Loop with Correction
def secure_extraction(raw_text):
    for i in range(3): # Max 3 attempts
        try:
            # Call LLM and ask for JSON...
            # result = llm.call(f"Extract info from: {raw_text}")
            
            # 3. VALDIATE
            user = UserInfo.model_validate_json(result)
            return user
        except Exception as e:
            print(f"Attempt {i+1} failed: {e}. Retrying with error feedback.")
            # Feed the error message back to the LLM
            # raw_text = f"The previous output failed validation: {e}. Please fix."

# 3. Test
# secure_extraction("My name is Sudeep and I am -5 years old.")
# The validator will catch the -5 and force the agent to rethink or error.

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Module 12 Summary

• Hallucinations are manageable if you use the right architectural patterns.
• Pydantic is your first line of defense for structured data.
• Self-Correction loops turn a "Fail" into a "Pass."
• Benchmarking is the only way to know if your agent is truly getting better.

Coming Up Next...

In Module 13, we leave the world of pure logic and enter the world of Safety and Security. We will learn about prompt injection, data privacy, and how to prevent your agents from "Go Rogue."

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Module 12 Checklist

• I understand the probabilistic nature of LLM hallucinations.
• I have set my project's temperature to 0.
• I can write a Pydantic model with a custom validator.
• I understand the cost of a "Self-Correction" loop.
• I have identified 10 "Ground Truth" test cases for my Capstone Project.