I built a search engine over 1,600+ cybersecurity articles — here's what I actually learned

GET /api/search?q=kerberoasting&cat=guide&limit=10

3. Fallback matters

Meilisearch goes down. Rarely, but it does. I added a MySQL LIKE fallback that kicks in automatically:

results, err := SearchMeilisearch(query, filters)

if err != nil || len(results) == 0 {

    results, err = SearchMySQL(query, filters) // fallback

}

Users never noticed the degradation. That's the goal.

The retrieval part: what "RAG" actually means at this scale

I see a lot of articles about building RAG systems with vector embeddings, chunking strategies, cosine similarity, etc. That's the right approach when your questions are complex and open-ended.

For a domain-specific article corpus with structured metadata, it's overkill. What I actually needed was:

• Fast keyword + semantic-ish retrieval (Meilisearch handles this with its ranking rules)


• A way to surface the right article given a user query


• Context injection into LLM prompts when generating summaries or related content

The architecture ended up being:

User query

    → Meilisearch (retrieval, ~10-30ms)

    → Top 3-5 articles (slug + title + excerpt)

    → LLM prompt context

    → Generated response / enriched content

No vector DB. No embeddings pipeline. No chunking headaches. For 1,600 articles averaging 2,000 words each, this works well.

Honest numbers

The 12MB index for 1,600+ articles is worth emphasizing — Meilisearch is lean.

What I'd do differently

1. Index full content, not just excerpts

I indexed titles, slugs, excerpts and tags — but not the full article body. This means searching for a technical term that appears deep in an article content returns nothing. I'm fixing this progressively.

2. Add synonyms from day one

Meilisearch has a synonyms API. I should have built a synonyms list for cybersecurity terminology immediately:

{

  "AD": ["Active Directory"],

  "pentest": ["penetration test", "intrusion test"],

  "MFA": ["multi-factor authentication", "2FA"]

}

I added these late, after noticing obvious query misses.

3. Log every failed search

The most valuable dataset I have is the list of searches that returned zero results. It tells you exactly what content you're missing and what synonyms to add. I started logging these to a search_misses table — should have done it from the start.

The takeaway

If you're building a content-heavy site and want good search without a massive infrastructure investment:

1. Meilisearch is genuinely good and genuinely easy


2. Content quality beats algorithmic cleverness every time


3. For domain-specific retrieval, you don't need vector embeddings unless your queries are conversational/open-ended


4. Log your zero-result searches — it's free product research

The full search endpoint with category/difficulty/type filters, pagination and Meilisearch/MySQL fallback is about 80 lines of Go. Happy to share if useful.

I run , cloud security and compliance — including 17 free security hardening checklists (PDF + Excel).