[DOCS] Add full-text search overview

docs/reference/analysis/tokenizers.asciidoc

@@ -1,11 +1,12 @@
 [[analysis-tokenizers]]
 == Tokenizer reference
 
+.Difference between {es} tokenization and neural tokenization
 [NOTE]
 ====
-{es}'s text analysis produces meaningful _linguistic_ tokens (like words and phrases) optimized for search relevance scoring.
-This differs from neural tokenizers, which break text into smaller subword units and numerical vectors for machine learning models.
-For example, "searching" becomes the searchable word token "search" in {es}, while a neural tokenizer might split it into ["sea", "##rch", "##ing"] for model consumption.
+{es}'s tokenization process produces linguistic tokens, optimized for search and retrieval.
+This differs from neural tokenization in the context of machine learning and natural language processing. Neural tokenizers translate strings into smaller, subword tokens, which are encoded into vectors for consumptions by neural networks.
+{es} does not have built-in neural tokenizers.
 ====
 
 A _tokenizer_ receives a stream of characters, breaks it up into individual

docs/reference/quickstart/full-text-filtering-tutorial.asciidoc

@@ -4,7 +4,7 @@
 <titleabbrev>Basics: Full-text search and filtering</titleabbrev>
 ++++
 
-This is a hands-on introduction to the basics of full-text search with {es}, also known as _lexical search_, using the <<search-search,`_search` API>> and <<query-dsl,Query DSL>>.
+This is a hands-on introduction to the basics of <<full-text-search,full-text search>> with {es}, also known as _lexical search_, using the <<search-search,`_search` API>> and <<query-dsl,Query DSL>>.
 You'll also learn how to filter data, to narrow down search results based on exact criteria.
 
 In this scenario, we're implementing a search function for a cooking blog.
@@ -632,6 +632,7 @@ This tutorial introduced the basics of full-text search and filtering in {es}.
 Building a real-world search experience requires understanding many more advanced concepts and techniques.
 Here are some resources once you're ready to dive deeper:
 
+* <<full-text-search, Full-text search>>: Learn about the core components of full-text search in {es}.
 * <<search-analyze, Elasticsearch basics — Search and analyze data>>: Understand all your options for searching and analyzing data in {es}.
 * <<analysis,Text analysis>>: Understand how text is processed for full-text search.
 * <<search-with-elasticsearch>>: Learn about more advanced search techniques using the `_search` API, including semantic search.

docs/reference/search/search-your-data/full-text-search.asciidoc

@@ -12,27 +12,29 @@ Full-text search, also known as lexical search, is a technique for fast, efficie
 Documents and search queries are transformed to enable returning https://www.elastic.co/what-is/search-relevance[relevant] results instead of simply exact term matches.
 Fields of type <<text-field-type,`text`>> are analyzed and indexed for full-text search.
 
-Built on decades of information retrieval research, full-text search in {es} is a compute-efficient, deterministic approach that scales predictably with data volume.
-Full-text search is the cornerstone of production-grade search solutions.
-Combine full-text search with <<semantic-search,semantic search using vectors>> to build modern hybrid search applications.
+Built on decades of information retrieval research, full-text search delivers reliable results that scale predictably as your data grows. Because it runs efficiently on CPUs, {es}'s full-text search requires minimal computational resources compared to GPU-intensive vector operations.
+
+This translates to lower infrastructure costs and predictable scaling requirements. You can scale horizontally by adding more nodes with standard CPU cores and RAM - no specialized hardware needed. A typical deployment will start with 2-3 nodes and grow incrementally as search volume increases. Learn more about <<scalability, moving to production>>.
+
+You can combine full-text search with <<semantic-search,semantic search using vectors>> to build modern hybrid search applications. While vector search may require additional GPU resources, the full-text component remains cost-effective by leveraging existing CPU infrastructure.
 
 [discrete]
 [[full-text-search-how-it-works]]
 === How full-text search works
 
-The following diagram illustrates the components of full-text search. Note that the query text also undergoes text analysis, so that it's transformed in the same way as the indexed text. 
+The following diagram illustrates the components of full-text search.
 
 image::images/search/full-text-search-overview.svg[Components of full-text search from analysis to relevance scoring, align=center, width=500]
 
 At a high level, full-text search involves the following:
 
-* <<analysis-overview,*Text analysis*>>: Analysis consists of a pipeline of sequential transformations. Text is transformed into a format optimized for searching by stemming, lowercasing, stop word elimination, etc. {es} contains a number of built-in <<analysis-analyzers,analyzers>> (including language-specific analyzers) and tokenizers, and you can also create custom analyzers.
+* <<analysis-overview,*Text analysis*>>: Analysis consists of a pipeline of sequential transformations. Text is transformed into a format optimized for searching using techniques such as stemming, lowercasing, and stop word elimination. {es} contains a number of built-in <<analysis-analyzers,analyzers>> and tokenizers, including options to analyze specific language text. You can also create custom analyzers.
 +
 [TIP]
 ====
 Refer to <<test-analyzer,Test an analyzer>> to learn how to test an analyzer and inspect the tokens and metadata it generates.
 ====
-* *Inverted index*: After analysis is complete, {es} builds an inverted index from the resulting tokens.
+* *Inverted index creation*: After analysis is complete, {es} builds an inverted index from the resulting tokens.
 An inverted index is a data structure that maps each token to the documents that contain it.
 It's made up of two key components:
 ** *Dictionary*: A sorted list of all unique terms in the collection of documents in your index.
@@ -47,23 +49,36 @@ Query DSL supports a number of <<full-text-queries,full-text queries>>.
 + 
 As of 8.17, {esql} also supports <<esql-search-functions,full-text search>> functions.
 
+[discrete]
+[[full-text-search-getting-started]]
+=== Getting started
+
+For a hands-on introduction to full-text search, refer to the <<full-text-filter-tutorial,full-text search tutorial>>.
+
 [discrete]
 [[full-text-search-learn-more]]
 === Learn more
 
-.Getting Started
-* <<full-text-filter-tutorial,Hands-on full-text search tutorial>> 
+Here are some resources to help you learn more about full-text search with {es}.
+
+*Core concepts*
+
+Learn about the core components of full-text search:
 
-.Core Concepts
 * <<text,Text fields>>
 * <<analysis,Text analysis>>
-* <<analysis-tokenizers,Tokenizers>>
-* <<analysis-analyzers,Analyzers>>
+** <<analysis-tokenizers,Tokenizers>>
+** <<analysis-analyzers,Analyzers>>
+
+*{es} query languages*
+
+Learn how to build full-text search queries using {es}'s query languages:
 
-.Search APIs
 * <<full-text-queries,Full-text queries using Query DSL>> 
 * <<esql-search-functions,Full-text search functions in {esql}>>
 
-.Advanced Topics
-* https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables[Practical BM25: Part 2 - The BM25 Algorithm and its Variables]
-* <<recipes,Search relevance optimization recipes>>
+*Advanced topics*
+
+For a technical deep dive into {es}'s BM25 implementation read this blog post: https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables[The BM25 Algorithm and its Variables].
+
+To learn how to optimize the relevance of your search results, refer to <<recipes,Search relevance optimizations>>.