Update README.md

README.md

@@ -22,8 +22,6 @@ The six intrinsics that have been implemented as LoRA adapters for `ibm-granite/
 
 **Query Rewrite (QR):** Given a conversation ending with a user query, QR will decontextualize that last user query by rewriting it (whenever necessary) into an equivalent version that is standalone and can be understood by itself. While this adapter is general purpose for any multi-turn conversation, it is especially effective in RAG settings where its ability to rewrite a user query into a standalone version directly improves the retriever performance, which in turn improves the answer generation performance. This is a *pre-retrieval* intrinsic since its suggested use is before invoking retrieval. 
 
-**Query Expansion (QE):** Given a conversation ending with a user query, QE is designed to probe the retriever from multiple angles by generating a set of semantically diverse versions of that last user query. This expanded set of queries provides diverse retrieval paths, and thus this intrinsic is particularly effective in RAG settings, especially with terse, general, or underspecified queries. Like Query Rewrite, this is a *pre-retrieval* intrinsic.
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 **Context Relevance (CR):** Given a conversation ending with a user query, and an individual passage, CR classifies whether the passage is relevant, partially relevant, or irrelevant for answering the last user query - or if the passage may instead mislead or harm the downstream generator model’s response quality. This is a *pre-generation* intrinsic.
 
 **Answerability Determination (AD):** Given a conversation ending with a user query, and a set of passages, AD classifies whether that final user query is answerable or unanswerable based on the available information in the passages. It is valuable for restraining over-eager models by identifying unanswerable queries and preventing the generation of hallucinated responses. It can also be used to indicate that the system should re-query the retriever with alternate formulations, to fetch more relevant passages. This is a *pre-generation* intrinsic.
@@ -38,6 +36,12 @@ The six intrinsics that have been implemented as LoRA adapters for `ibm-granite/
 
 The recommended use of these LoRA adapters is through [Granite IO Processing](https://github.com/ibm-granite/granite-io), a framework which enables transforming how a user calls or infers an IBM Granite model and how the output from the model is returned to the user. In other words, the framework allows extended functionality of calling the model. This is particularly valuable as the downstream use of intrinsics relies on correctly structured output. Although we have made the individual LoRAs available, we strongly suggest that everyone use the implementations in Granite IO and we have made example notebooks available. Two additional intrinsics, Query Expansion and Passage Reranking, are pure software-based implementations and are only available through Granite IO.
 
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+## Intrinsics implemented as functions
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+**Query Expansion (QE):** Given a conversation ending with a user query, QE is designed to probe the retriever from multiple angles by generating a set of semantically diverse versions of that last user query. This expanded set of queries provides diverse retrieval paths, and thus this intrinsic is particularly effective in RAG settings, especially with terse, general, or underspecified queries. Like Query Rewrite, this is a *pre-retrieval* intrinsic.
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 ## Quickstart Example
 
 As explained above, it is highly recommended to use these LoRA adapters through [Granite IO Processing](https://github.com/ibm-granite/granite-io).