Robert's feedback

Author: HeidiSteen

articles/search/hybrid-search-ranking.md

@@ -55,7 +55,7 @@ The following chart identifies the scoring property returned on each match, algo
 |---------------|-----------|-------------------|-------|
 | full-text search | `@search.score` | BM25 algorithm | No upper limit. |
 | vector search | `@search.score` | HNSW algorithm, using the similarity metric specified in the HNSW configuration. | 0.333 - 1.00 (Cosine), 0 to 1 for Euclidean and DotProduct. | 
-| hybrid search | `@search.score` | RRF algorithm | Upper limit is bounded by the number of queries being fused, with each query contributing a maximum of approximately 1 to the RRF score. Merging 11 queries produces higher scores than if only two search results are merged.|
+| hybrid search | `@search.score` | RRF algorithm | Upper limit is bounded by the number of queries being fused, with each query contributing a maximum of approximately 1 to the RRF score. Merging 3 queries produces higher scores than if only two search results are merged.|
 | semantic ranking | `@search.rerankerScore` | Semantic ranking | 1.00 - 4.00 |
 
 Semantic ranking doesn't participate in RRF. Its score (`@search.rerankerScore`) is always reported separately in the query response. Semantic ranking can rerank full text and hybrid search results, assuming those results include fields having semantically rich content.

articles/search/retrieval-augmented-generation-overview.md

@@ -52,7 +52,7 @@ A high-level summary of the pattern looks like this:
 + Send the top ranked search results to the LLM.
 + Use the natural language understanding and reasoning capabilities of the LLM to generate a response to the initial prompt.
 
-Cognitive Search provides inputs and prompts to the LLM but doesn't train the model. In RAG architecture, there's no extra training. The LLM is pretrained using public data, but it generates responses that are augmented by information from the retriever.
+Cognitive search provides inputs to the LLM prompt, but doesn't train the model. In RAG architecture, there's no extra training. The LLM is pretrained using public data, but it generates responses that are augmented by information from the retriever.
 
 RAG patterns that include Cognitive Search have the elements indicated in the following illustration.