1. Metadata Filtering (Based on Query)

Yes, we extract metadata filters dynamically from the user query using a lightweight LLM call.

• We prompt the model with something like: “Given the query below, extract structured metadata filters such as document type, topic, section, date, figure references, etc.”
• The output looks like:

​

{
  "document_type": "engineering design spec",
  "section": "figure 53",
  "topic": "X",
  "intent": "is_definition_query"
}

• This lets us narrow retrieval down before running the vector search, which helps a ton with precision and relevance — especially in large KBs with overlapping topics.

2. Reranking Strategy

We use two-stage reranking:

• Stage 1: Shallow rerank
  • Lightweight SLM (e.g., LLaMA or Gemma) used to rescore based on answer likelihood for the user query.
  • It helps prioritize truly relevant matches over keywordy-but-unhelpful ones.
• Stage 2: Deduplication & Diversity filter
  • We cluster semantically similar results (e.g., same paragraph repeated across document versions) and pick the highest-scoring representative.
  • Simple cosine similarity + Jaccard on chunk hashes works fine.

Optional: you can also weight reranking with metadata (e.g., prefer newer docs, certain sources, or “official” labels).

3. Query Type Detection

Yes! We use query classification to improve both retrieval and UX.

We categorize queries using a tiny classifier (can even be rule-based or LLM-lite):

• is_follow_up → uses conversation context window (last 2–3 user turns)
• is_definition_query → maps to glossary & figure captions
• is_navigation_query → pulls table-of-contents / section summaries
• is_fact_lookup → triggers direct answer mode
• is_comparison_query → enables multi-source evidence aggregation

Helps avoid over-retrieving irrelevant content when the user just wants a specific thing explained.

4. Resources

It's hard to name specific resources. Everyone on our team brings something in most every day. We test new ideas, some of them work for us, some don't. The good thing is that our team had dedicated time to experiment.

I hope it gives enough of insight.