[incubator-nlpcraft-website] branch master updated: Update docs.html

[ar...@apache.org]

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aradzinski pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-nlpcraft-website.git


The following commit(s) were added to refs/heads/master by this push:
     new b38e440  Update docs.html
b38e440 is described below

commit b38e4409e4c885fbd95e5bc094395042f3de5d47
Author: Aaron Radzinski <ar...@apache.org>
AuthorDate: Sun Mar 21 20:00:20 2021 -0700

    Update docs.html
---
 docs.html | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/docs.html b/docs.html
index 000203f..06408d6 100644
--- a/docs.html
+++ b/docs.html
@@ -27,21 +27,23 @@ id: overview
         <p>
             Apache NLPCraft is a Java-based <a target=_blank href="https://www.apache.org/licenses/">open source</a> library
             for adding a natural language interaction interface to any applications. It can connect with
-            any private or public data source, and has no hardware or software lock-in. It is based on advanced intent-based matching
+            any private or public data source, and has no hardware or software lock-in. Its design based on advanced
+            <a href="/intent-matching.html">Intent Definition Language</a> (IDL) for defining non-trivial intents and unique fully deterministic intent matching
             of the input utterances. You can build intents for NLPCraft using any JVM-based languages like Java, Scala, Kotlin, Groovy, etc. NLPCraft
             exposes REST APIs for integration with end-user applications that can be written in any language or system.
         </p>
         <p>
-            One of the key features of NLPCraft is its use of deterministic intent matching that is tailor made for
-            domain-specific natural language interface. It doesn't force developers to use direct deep learning
-            approach that involves time consuming model training or corpora development - resulting in much <em>simpler <span class="amp">&</span> faster</em>
-            implementation.
+            One of the key features of NLPCraft is its use of IDL coupled with deterministic intent matching that are tailor made for
+            domain-specific natural language interface. This design doesn't force developers to use direct deep learning
+            approach with time consuming corpora development and model training - resulting in much a
+            <em>simpler <span class="amp">&</span> faster</em> implementation.
         </p>
         <p>
             Another key aspect of NLPCraft is its initial focus on processing English language. Although it may sound
             counterintuitive, this narrower initial focus enables NLPCraft to deliver unprecedented ease of use combined with
             unparalleled comprehension capabilities for English input out-of-the-box. It avoids watered down functionality and overly
-            complicated configuration, training and usage. English language is spoken by more
+            complicated configuration and usage - following on project's "built for engineers by engineers" ethos.
+            English language is spoken by more
             than a billion people on this planet and is de facto standard global language of the business and commerce.
         </p>
         <p>
@@ -63,16 +65,16 @@ id: overview
     <section id="data-model">
         <h3 class="section-title">Data Model</h3>
         <p>
-            NLPCraft employs model-as-a-code approach where entire data model is an implementation of
+            NLPCraft employs model-as-a-code approach where everything you do in NLPCraft is part of your source code. Data model is an implementation of
             <a target="javadoc" href="/apis/latest/org/apache/nlpcraft/model/NCModel.html">NCModel</a> Java interface that
             can be developed using any JVM programming language like Java, Scala, Kotlin or Groovy.
-            Data model defines named entities, various configuration properties as well as intents that use defined named entities. Model-as-a-code natively supports
+            Data model defines named entities, various configuration properties as well as intents to interpret user input. Model-as-a-code natively supports
             any software lifecycle tools and frameworks in Java ecosystem.
         </p>
         <p>
             Typically, declarative portion of the model will be stored in a separate JSON or YAML file
             for simpler maintenance. There are no practical limitation on how complex or simple a model
-            can be, or what other tools it can use. Data models use comprehensive <a href="/intent-matching.html">intent-based matching</a>.
+            can be, or what other tools it can use. Data models use <a href="/intent-matching.html">intents</a> to match the user input.
         </p>
         <p>
             To use data model it has to be deployed into a data probe.
@@ -81,7 +83,7 @@ id: overview
     <section id="data-probe">
         <h3 class="section-title">Data Probe</h3>
         <p>
-            Data probe is a light-weight container designed to securely deploy and manage data models.
+            Data probe is a light-weight container designed to securely deploy and manage user data models.
             Each probe can deploy and manage multiple models and many probes can be connected to the REST server (or a cluster of REST servers).
             The main purpose of the data probe is to separate data model hosting from managing REST calls from the clients.
             While you would typically have just one REST server, you may have multiple data probes deployed