How to Reduce LLM Hallucinations with Authority-Ranked Sources

The hallucination problem

Large language models hallucinate. They generate fluent, confident-sounding text that is factually wrong: invented citations, misattributed quotes, non-existent studies. This is not a failure of generation. It is a failure of grounding.

When an LLM is asked "who are the leading experts on quantum error correction?", it draws on patterns in its training data. If those patterns are noisy, incomplete, or out of date, the output reflects that noise. The model is not lying, it is doing exactly what it was trained to do. The problem is upstream, in the data.

Why RAG alone is not enough

Retrieval-Augmented Generation (RAG) improves accuracy by injecting external context into the prompt at inference time. Instead of relying solely on parametric knowledge, the model retrieves relevant documents and reasons over them.

The problem: most RAG pipelines retrieve from web search APIs. Web search returns whatever ranks highest by PageRank, recency, or SEO optimisation, not by expertise. A well-ranked blog post written by an anonymous content farm will score equally with a peer-reviewed paper by the world's leading expert on the topic. The retrieval step does not discriminate.

This is the garbage-in, garbage-out problem for AI. Your LLM is only as grounded as the sources you feed it. If your retrieval layer returns low-quality content, your generation layer will produce low-quality answers: confidently, fluently, and wrongly.

The authority approach

Rather than searching for documents about a topic, what if your retrieval step returned the verified humans who are actually authoritative on that topic?

Authority-ranked sources have a fundamentally different signal profile. They have been validated by peer recognition, citation counts, follower networks of other verified experts, and sustained output over time, not by SEO. When your LLM is asked to reason about quantum computing and its context includes Stuart Russell, John Preskill, and Peter Shor with their verified credentials, the probability of hallucinated attribution drops sharply.

There are two concrete reasons for this:

• Disambiguation. An authority-ranked expert profile resolves ambiguity. "Stuart Russell" the AI safety researcher is distinct from other people with that name. A verified profile with authority rank, country, and topic makes the identity deterministic.
• Signal quality. An expert ranked #1 on quantum computing by the Amygdala index has earned that rank through real-world recognition. The LLM injecting that context is working with a quality signal, not a web search result.

How Amygdala fits into a RAG pipeline

The Amygdala Authority Index exposes a REST API with four endpoints. The /index/ endpoint returns authority-ranked experts for any topic query. You can integrate it into your existing RAG pipeline in a single additional step: before building your prompt context, call Amygdala to establish who the verified authorities are.

import requests
from mistralai import Mistral

AMYGDALA_API_KEY = "amyg_..."
MISTRAL_API_KEY  = "..."

mistral = Mistral(api_key=MISTRAL_API_KEY)

def get_authorities(query: str, limit: int = 5) -> list:
    resp = requests.get(
        "https://api.amygdala.eu/api/v1/index/",
        params={"query": query, "limit": limit},
        headers={"Authorization": f"Bearer {AMYGDALA_API_KEY}"},
    )
    resp.raise_for_status()
    return resp.json()["results"]

def answer_with_authorities(query: str) -> str:
    # Step 1: get verified authorities on the topic
    authorities = get_authorities(query)

    # Step 2: build authority context
    context = "\n".join(
        f"{a['rank']}. {a['name']} ({a['country_name']}), "
        f"verified authority on {a['topic']}"
        for a in authorities
    )

    # Step 3: call Mistral with grounded authority context
    response = mistral.chat.complete(
        model="mistral-large-latest",
        messages=[{
            "role": "user",
            "content": (
                f"Question: {query}\n\n"
                f"Verified authorities on this topic (ranked):\n"
                f"{context}\n\n"
                "Answer the question. Cite only the listed authorities by name."
            ),
        }],
    )
    return response.choices[0].message.content

print(answer_with_authorities("What are the key risks of advanced AI?"))

This context block gives your LLM a structured, verified list of who to cite, before it ever starts generating. The model can now attribute claims to real, ranked experts rather than inventing them from training noise.

What this does not fix

Authority-ranked grounding reduces hallucinations in expert attribution and source credibility. It does not eliminate all forms of hallucination. LLMs can still misrepresent what an expert has said, confuse similar concepts, or reason incorrectly from accurate premises. Grounding is a necessary condition for accuracy, not a sufficient one.

For highest-stakes applications, combine authority-ranked context injection with output verification: have a second model pass check claims against the retrieved expert profiles before serving the response to users.

Conclusion

Hallucinations are a retrieval problem as much as a generation problem. The fix is not just better models, it is better data. Authority-ranked expert sources give your LLM something to reason against that carries a quality signal by construction.

Start with a single topic and compare the outputs. The difference is measurable.