Stop Returning Text from RAG: Implementing Typed Answer Contracts to Prevent Hallucination

In the world of Retrieval-Augmented Generation (RAG), we have spent years perfecting the 'Retrieval' part. We optimize vector databases, experiment with hybrid search, and fine-tune rerankers. However, the 'Generation' part—the final mile where the Large Language Model (LLM) speaks to the user—remains a Wild West of unstructured strings. If you are building enterprise-grade document intelligence, returning raw text from your RAG pipeline is no longer acceptable. It is the primary source of hallucinations, downstream parsing errors, and system fragility.

To build robust systems, we must transition from 'Chatty AI' to 'Contractual AI.' This is achieved through a Typed Answer Contract (TAC). By defining a strict schema for every response, we transform the LLM from a creative writer into a structured data extractor that is verifiable, testable, and reliable. Leveraging platforms like n1n.ai allows developers to seamlessly switch between high-performance models like DeepSeek-V3 and Claude 3.5 Sonnet to enforce these contracts at scale.

The Problem: The 'Textual Void' in RAG

Standard RAG pipelines typically end with a prompt like: 'Based on the context, answer the user question.' The LLM then generates a paragraph of text. While this looks good in a demo, it is a nightmare for production for three reasons:

1. Hallucination Camouflage: LLMs are excellent at sounding confident while being wrong. In a block of text, a false date or a wrong price is hard to catch programmatically.
2. Downstream Failure: If your application needs to trigger a workflow (e.g., 'If the contract expires before 2026, send an alert'), parsing that logic from a natural language paragraph is error-prone.
3. Lack of Accountability: You cannot easily unit test a paragraph. You can, however, unit test a JSON object with specific types.

The Solution: The Schema is the Contract

A Typed Answer Contract treats every field in a JSON schema as a specific question the pipeline asks the model. Instead of asking 'What does this document say?', we ask the model to fill a Pydantic model. This forces the model to categorize its knowledge and explicitly state when information is missing.

When using n1n.ai, you can leverage unified API endpoints that support structured outputs across multiple providers, ensuring that whether you use OpenAI o3 or DeepSeek-V3, the contract remains valid.

Implementation Guide: Building a Typed RAG Pipeline

Let’s look at how to implement this using Python and Pydantic. We will define a contract for an insurance document extraction task.

Step 1: Define the Contract

from pydantic import BaseModel, Field, validator
from typing import List, Optional

class PolicyAnalysis(BaseModel):
    policy_holder: str = Field(..., description="Full name of the insured entity")
    coverage_amount: float = Field(..., description="Maximum liability limit in USD")
    exclusions: List[str] = Field(default_factory=list, description="List of specific scenarios not covered")
    is_expired: bool = Field(..., description="True if the current date is past the expiration date")
    confidence_score: float = Field(..., description="Model's internal confidence from 0 to 1")

    @validator('confidence_score')
    def check_confidence(cls, v):
        if v < 0.5:
            raise ValueError('Confidence too low for automated processing')
        return v

Step 2: Executing via n1n.ai

Using the n1n.ai API, we can send this schema to a powerful model like DeepSeek-V3. The benefit of using an aggregator like n1n.ai is the ability to fall back to Claude 3.5 Sonnet if one model fails to respect the schema.

import requests
import json

# n1n.ai API Configuration
API_KEY = "YOUR_N1N_API_KEY"
URL = "https://api.n1n.ai/v1/chat/completions"

def get_structured_rag_response(context, question):
    payload = {
        "model": "deepseek-v3",
        "messages": [
            {"role": "system", "content": "You are a legal analyst. Extract data strictly according to the schema."},
            {"role": "user", "content": f"Context: {context}\n\nQuestion: {question}"}
        ],
        "response_format": {"type": "json_object"},
        "schema": PolicyAnalysis.schema() # Pass the Pydantic schema
    }

    headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
    response = requests.post(URL, json=payload, headers=headers)
    return PolicyAnalysis.parse_raw(response.json()['choices'][0]['message']['content'])

Advanced Technique: The 'Evidence' Field

To truly eliminate hallucinations, add an evidence field to your schema for every data point. This forces the LLM to quote the source text directly from the retrieved context. If the model cannot find a direct quote, it shouldn't fill the field.

class EvidenceField(BaseModel):
    value: str
    source_quote: str = Field(..., description="Direct snippet from the context proving this value")
    page_number: int

Comparison: Text-based vs. Typed RAG

Pro Tips for Success

1. Small Schemas: Don't try to extract 50 fields at once. The more fields in the contract, the higher the chance the LLM loses focus. Break complex documents into multiple extraction passes.
2. Use DeepSeek-V3 for Cost-Efficiency: For high-volume extraction, DeepSeek-V3 via n1n.ai offers incredible performance at a fraction of the cost of other frontier models.
3. Graceful Degradation: If a model fails to return valid JSON, implement a retry logic that switches to a 'reasoning' model like OpenAI o3 to debug the extraction.

Conclusion

The era of letting LLMs ramble is over. For enterprise applications, the schema is the contract. By enforcing typed answers, you ensure that your RAG pipeline is not just a fancy search engine, but a reliable data processing engine. Start building your contractual AI pipelines today by leveraging the multi-model capabilities of n1n.ai.

Get a free API key at n1n.ai