Building a RAG Pipeline with Trusted, Authority-Ranked Data

Your RAG pipeline's weakest link

Retrieval-Augmented Generation pipelines fail in one of two ways: the generation step produces poor output, or the retrieval step returns poor data. Most debugging effort goes into the generation step: prompt engineering, model selection, temperature tuning. But for many applications, the retrieval step is the real bottleneck.

The retrieval step determines what your LLM reasons over. If it returns noisy, low-quality, or irrelevant data, no amount of prompt engineering will produce accurate, trustworthy output. The model is doing its job, working with the context you gave it.

The web data quality problem

The default choice for RAG retrieval is a web search API. It is the obvious starting point: general, comprehensive, and easy to integrate. But web search was designed for humans browsing with intent, not for AI systems that need structured, high-quality signal to ground reasoning.

Web search results have several properties that make them a poor RAG data source:

• SEO optimisation. Pages rank by signals that correlate with click-through rates, not accuracy. Well-optimised misinformation can outrank authoritative primary sources.
• AI-generated content. A growing percentage of web content is generated by LLMs trained on other LLMs. Injecting this into your RAG pipeline creates a feedback loop of low-quality signal.
• Temporal decay. Web search returns the most recently indexed content. For many topics, the most authoritative sources are older (research papers, established experts) and rank below fresher but less reliable content.
• No credibility signal. A link from a major news site and a link from a random blog look identical to most RAG pipelines. The retrieval step provides no credibility gradient.

Authority-ranked data as a RAG source

Instead of asking "what pages are about this topic?", ask "who are the verified authorities on this topic?". Authority-ranked data inverts the quality problem. Rather than returning everything and leaving your LLM to sort signal from noise, it returns verified humans with established authority: people whose output carries a quality signal by construction.

For applications that involve citing sources, attributing claims, or reasoning about who knows what, this is a fundamentally better retrieval layer. The data structure is also different: instead of raw text snippets, you get structured profiles with name, rank, country, verified social handles, bio, and topic. Structured data is more useful to LLMs than raw text: deterministic, deduplicated, and unambiguous.

Integration pattern

Enriched pipeline: add a Google search per authority

Authority profiles tell you who the authorities are. Adding a targeted Google search per authority tells you what they have been publishing lately. The key insight is the search query: instead of searching for the topic broadly, search for "mRNA {expert_name}". This anchors the results to that specific authority's work, so the sources you feed to Mistral are both recent and attributed to a verified authority rather than any random article that ranks for the topic.

Social media platforms and aggregator sites are excluded from the search results: they add noise without adding signal for a research query. The result is a context block that combines verified identity (from Amygdala) with the authority's top recent web presence, giving Mistral the clearest possible picture of what leading oncologists are saying right now.

import requests
from mistralai import Mistral

AMYGDALA_API_KEY    = "amyg_..."
MISTRAL_API_KEY     = "..."
GOOGLE_API_KEY      = "..."
GOOGLE_CSE_ID       = "..."

mistral = Mistral(api_key=MISTRAL_API_KEY)

# Sites that add noise rather than signal for research queries
EXCLUDED_SITES = [
    "reddit.com", "quora.com", "pinterest.com",
    "facebook.com", "instagram.com", "tiktok.com",
    "twitter.com", "x.com", "linkedin.com",
]

def google_search(query: str, num: int = 3) -> list[dict]:
    """Search Google Custom Search and return top results."""
    exclusions = " ".join(f"-site:{s}" for s in EXCLUDED_SITES)
    resp = requests.get(
        "https://www.googleapis.com/customsearch/v1",
        params={
            "key": GOOGLE_API_KEY,
            "cx":  GOOGLE_CSE_ID,
            "q":   f"{query} {exclusions}",
            "num": num,
            "dateRestrict": "y1",   # last 12 months
            "safe": "active",
        },
    )
    resp.raise_for_status()
    return resp.json().get("items", [])

def rag_answer_with_search(query: str) -> str:
    # Step 1: get top verified authorities for this query
    authorities = requests.get(
        "https://api.amygdala.eu/api/v1/index/",
        params={"query": query, "limit": 25},
        headers={"Authorization": f"Bearer {AMYGDALA_API_KEY}"},
    ).json()["results"]

    # Step 2: fetch all profiles in one request
    sdus    = [a["sdu"] for a in authorities]
    details = {
        r["sdu"]: r
        for r in requests.get(
            "https://api.amygdala.eu/api/v1/detail/",
            params={"sdus": sdus},
            headers={"Authorization": f"Bearer {AMYGDALA_API_KEY}"},
        ).json()["results"]
    }

    context_parts = []
    for a in authorities:
        detail = details.get(a["sdu"], {})
        bio = detail.get("bio", "")

        # Search for recent content by this specific expert
        results = google_search(f"mRNA {a['name']}", num=3)
        sources = "\n".join(
            f"  - {r['title']}: {r['link']}"
            for r in results
        ) or "  No recent sources found."

        context_parts.append(
            f"Expert #{a['rank']}: {a['name']} ({a['country_name']})\n"
            f"Bio: {bio}\n"
            f"Recent sources:\n{sources}"
        )

    context = "\n\n---\n\n".join(context_parts)

    # Step 3: call Mistral with verified expert profiles + their top sources
    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}"
            ),
        }],
    )
    return response.choices[0].message.content

query = "What are the latest developments in mRNA vaccines for cancer treatment?"
print(rag_answer_with_search(query))

MCP for Claude and agent frameworks

If you are building with Claude or any MCP-compatible framework, Amygdala has a native MCP server with four tools: search_authorities, get_authority_detail, find_peers, and match. Install it once and your agent can call Amygdala in plain language, with no additional integration code. See the MCP documentation for setup instructions.

EU compliance bonus

For teams building in the European Union, the data source you use in your RAG pipeline has compliance implications. If your retrieval step sends queries containing personal data to a US-based API, you are transferring personal data to a third country under GDPR, which requires Standard Contractual Clauses and a data transfer impact assessment.

Amygdala runs entirely on European infrastructure: Hetzner (Finland and Germany) for compute and storage, Mistral AI (Paris) for inference, Weaviate (Amsterdam) for vector database. There are no US data transfers. Your RAG pipeline stays GDPR-compliant end to end. See our European infrastructure page for the full architecture.

Where to go from here

The fastest way to evaluate authority-ranked data in your pipeline is to run it in parallel with your current retrieval step and compare outputs qualitatively. Pick ten domain-specific queries from your application, ideally ones where expertise matters, like medical, legal, scientific, or financial topics, run both retrievers, and compare the context quality before it reaches the LLM. The difference in source credibility is usually visible immediately.