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2025-12-11

Building an AI Assurance Pipeline for LLM Outputs

AI AssuranceLLMEngineeringArchitectureProductAssurenest AI

LLMs are powerful, but they are also unpredictable. They can hallucinate facts, leak sensitive data, produce biased language, or quietly rewrite the intent of a prompt. Businesses experimenting with LLMs are starting to realize that a fast response isn't enough; you need a way to check the output and build trust in how the system behaves.

This post walks through how I built a practical AI assurance pipeline as part of Assurenest AI — a browser extension + dashboard that monitors LLM outputs in real-time and evaluates them for risk and quality.

Why Assurance Matters

When a human interacts with a model like ChatGPT or Claude, they can visually detect if the answer "feels wrong." When you embed an LLM inside an application, there is no human in the loop. There’s also no audit trail. If the model hallucinates, makes up citations, or leaks PII, nobody notices until it's too late.

Assurance provides three layers:

  1. Detection — what happened?
  2. Explanation — why does it matter?
  3. Logging — can we prove it later?

This is not about replacing the model; it’s about adding safety rails around it.

Architecture Overview

The pipeline I built follows a simple flow:

  1. Capture (Browser Extension)
    Intercepts prompt + response pairs from ChatGPT.

  2. Evaluate (Backend)
    Runs detection modules:

    • hallucination
    • bias
    • PII
    • sentiment
    • consistency / tone

  3. Explain (Dashboard)
    Displays findings as:

    • flags
    • scores
    • risk categories
    • justifications

  4. Persist (Firestore)
    Stores historical artifacts for compliance and debugging.

  5. Review (Audit UI)
    Allows humans to inspect, export, or compare outputs.

This structure allows future checks to be plugged in as modules.

Detection Modules

Hallucination

Hallucination is not binary. The model may be incorrect, uncertain, or confidently wrong. I found that comparing statements against structured knowledge sources and confidence levels worked better than pure classifiers.

Bias

Bias detection is easier to demonstrate than fully solve. The module flags language associated with demographic, political, or cultural assumptions and categorizes style, not morality.

PII

PII detection is the most straightforward. Regex + statistical filters + a small ML component work well for names, phone numbers, emails, and IDs.

Dashboard & Audit

The dashboard matters more than the pipeline. Without a UI:

  • you can't review cases
  • you can't learn failure modes
  • you can't explain behavior to non-engineers

I designed the dashboard like a compliance panel rather than a chatbot UX, because the point is transparency, not conversation.

Lessons Learned

  1. Observability beats accuracy
    Even a half-accurate detector is more useful than a black box.

  2. Audit trails change incentives
    Logging makes teams think differently about production use of LLMs.

  3. Assurance is product, not research
    The value is in workflows, not papers.

  4. Modular design scales
    New checks (quality, sentiment, toxicity) fit naturally into the pipeline.

Closing Thoughts

LLMs aren’t going away, and neither are their failure modes. If companies want to deploy them in workflows that touch compliance, finance, HR, healthcare, or policy, assurance becomes a requirement. The industry will eventually standardize around it — I just wanted to build a working prototype first.

Assurenest AI is still evolving, but the core idea remains the same:
make AI observable, explainable, and accountable.