Update README.md

README.md

@@ -145,8 +145,59 @@ For our guiding example we assume the context of this use-case is a Question-Ans
 **Step 1:**
 
 Embed the Query
+```
 "input": "Which country is Galileo from?"
-→ Embedding: [-0.6780134, 0.61449033, 0.102911085, ...]
+```
+→ Embedding: ```[-0.6780134, 0.61449033, 0.102911085, ...]```
+
+**Step 2:**
+
+Embed the Documents
+"input": "Galileo is a German television program series ..." 
+→ Embedding: ```[-0.36119246, 0.7793595, -0.38735497, ...]```
+"input": "Galileo di Vincenzo Bonaiuti de' Galilei ..."
+→ Embedding: ```[-0.25108248, 1.0496024, -0.20945309, ...]```
+
+**Step 3:**
+
+Compare the similarity
+A typical similarity measure between vectors is cosine similarity. Higher numbers 
+indicate more similar vectors and by extension capture the concept of relevance. 
+In a RAG application these scores determine the ranking during the retrieval step. 
+In this example, we obtain the following cosine similarities:
+Query vs. German TV show: ~0.661
+Query vs. Italian polymath: ~0.757
+This implies that the paragraph about the Italian polymath would be ranked higher than the paragraph 
+about the German TV show which is the one we’re interested in.
+
+#### Customized Embeddings
+
+To further improve performance you can use instructions to steer the model. Instructions can help the model 
+understand nuances of your specific data and ultimately lead to embeddings that are more useful for your use-case. 
+In this case, we aim to get embeddings that would lead to ranking the paragraph about the German TV Show higher 
+than the paragraph about the Italian polymath.
+**Step 1:**
+Embed the Query with an Instruction
+```"instruction": "Represent the question about TV shows to find a paragraph that answers it."```
+```"input": "input": "Which country is Galileo from?"```
+→ Embedding: ```[-0.6310919, 1.4309896, -0.85546875, ...]```
+**Step 2:**
+Compare the similarity
+We leave the embeddings of the documents untouched and now obtain the following cosine similarities:
+Query vs. German TV show: ~0.632
+Query vs. Italian polymath: ~0.512
+These new cosine similarities imply that the ranking has indeed changed and the paragraph about the German TV show is now more relevant. This shows that instructions can help the model understand nuances in the data better and ultimately lead to embeddings that are more useful for your use-case. 
+Tips on using the model
+
+
+First try and ideally evaluate the model on your data without instructions to see whether performance aligns with your expectations out-of-the-box
+If you decide to use an instruction with the aim of further boosting performance we suggest using this template as a guideline
+Template: Represent the [X] to find a [Y] that [describe how the X and Y relate] 
+Examples
+Represent the newspaper paragraph to find a newspaper paragraph with the same topic
+Represent the sentence to find another sentence with the same meaning
+In cases where the two texts to compare are different in nature (e.g. query and document) – also called “asymmetric” – we suggest to first add an instruction to query texts only. Again, try and ideally evaluate the model in this setting. Then, if your aim is to further boost performance, we suggest that you add instructions to document texts as well where [X] and [Y] are flipped accordingly.
+