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 If those patterns are noisy, incomplete, or out of date, the output reflects that noise.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 authorities, 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 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 authority 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 most direct way to reduce hallucinated attributions is the /match/ endpoint: your pipeline already has a list of author names and handles attached to the content it retrieved. Pass those authors, by name or by social media handle, to Amygdala and only keep the ones that come back verified. Names are the most natural input: "Geoffrey Hinton", "Yann LeCun", "Sam Altman". Name-based matching is coming soon and will be the primary way to use the API. Social media handles (X/Twitter, Instagram, YouTube, and others) are supported today. Unmatched entries are quietly dropped. The LLM then only cites people who are confirmed to be real, ranked authorities.

This is a stronger guarantee than injecting a generic list of top authorities: you are not adding outside names, you are verifying the specific people already present in your context.

import requests
from mistralai import Mistral

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

mistral = Mistral(api_key=MISTRAL_API_KEY)

def match_names_and_handles(handles: list[str]) -> list[dict]:
    """Match a list of author names or handles. Returns only verified authority profiles.
    Names (e.g. 'Geoffrey Hinton') are the primary input: coming soon.
    Social media handles (X/Twitter, Instagram, YouTube, and others) are supported today."""
    resp = requests.get(
        "https://api.amygdala.eu/api/v1/match/",
        params={"handles": handles},
        headers={"Authorization": f"Bearer {AMYGDALA_API_KEY}"},
    )
    resp.raise_for_status()
    return resp.json().get("results", [])

def answer_with_verified_authors(query: str, handles: list[str], context: str = "...") -> str:
    # Step 1: match all names and handles in one request
    # only authors that come back verified are trusted, unmatched are dropped
    verified = match_names_and_handles(handles)

    # Step 2: build a verified-authors block from the matched profiles
    if verified:
        authority_context = "\n".join(
            f"- {a['name']} (rank {a['rank']}, verified authority on {a['topic']})"
            for a in verified
        )
    else:
        authority_context = "No verified authorities found for the provided names and handles."

    # Step 3: answer using only context from verified authors
    # 'context' is whatever your pipeline retrieved: replace "..." with your content
    response = mistral.chat.complete(
        model="mistral-large-latest",
        messages=[{
            "role": "user",
            "content": (
                f"Answer this query based on the content below.\n\n"
                f"Query: {query}\n\n"
                f"Context from your pipeline:\n{context}\n\n"
                f"Verified authors in this context:\n{authority_context}\n\n"
                "Only cite authors listed as verified above."
            ),
        }],
    )
    return response.choices[0].message.content

# Pass names or social media handles: names are the most natural input.
# Name-based matching is coming soon; handles are supported today.
handles = [
    "Geoffrey Hinton",    # name-based matching: coming soon
    "Yann LeCun",         # name-based matching: coming soon
    "Sam Altman",         # name-based matching: coming soon
    "Yoshua Bengio",      # name-based matching: coming soon
    "Andrej Karpathy",    # name-based matching: coming soon
    "Demis Hassabis",     # name-based matching: coming soon
    "geoffreyhinton",     # social media handle (supported now)
    "ylecun",             # social media handle (supported now)
    "sama",               # social media handle (supported now)
    "yoshuabengio",       # social media handle (supported now)
    "karpathy",           # social media handle (supported now)
]
print(answer_with_verified_authors(
    query="What are the key risks of advanced AI?",
    handles=handles,
    context="...",
))

The context parameter is whatever your pipeline already retrieved: documents, search results, scraped articles. The handles list is the set of author names and handles attached to that content. Amygdala verifies which names and handles belong to real authorities. Only verified authors are passed to the model as citable sources.

What this does not fix

Authority-ranked grounding reduces hallucinations in authority attribution and source credibility. It does not eliminate all forms of hallucination. LLMs can still misrepresent what an authority 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 authority 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.