Title: Discussion 6 — Tuning Fusion for Your Query Mix
Course: AIINFRA 301: Production RAG & LLMOps — Observability and Evaluation > Section: Week 06 — Hybrid Search: Combining Dense Retrieval with BM25

💬 Discussion 6 — Tuning Fusion for Your Query Mix (20 pts)

Imagine your production query logs show mostly exact product-code lookups, with only occasional paraphrased natural-language questions mixed in. Compare that to a general-purpose FAQ bot, where most queries are conversational paraphrases and exact-ID lookups are rare. In your initial post (150+ words):

- Would you adjust Weaviate's alpha parameter differently for the product-code-heavy system versus the FAQ bot? In which direction, and why?
- Would you adjust the RRF k constant differently between the two systems? Recall that k in the range \[40, 80\] performs comparably on TREC data — is this a lever worth tuning at all for these use cases, or is there a more impactful configuration choice?
- Give a concrete example query from each scenario and describe what you'd expect to happen at your chosen settings.

Then respond to at least two classmates — push back or build on their reasoning, especially if someone suggests tuning k when the bigger lever is actually alpha (or vice versa).