guide: edit Exp Mgmt index (intro)

content/docs/user-guide/basic-concepts/experiment.md

@@ -1,11 +1,12 @@
 ---
 name: Experiment
-match: [experiment, experiments]
+match: [experiment, experiments, 'DVC experiments']
 tooltip: >-
   An attempt to reach desired/better/interesting results during data pipelining
   or ML model development. DVC is designed to help [manage
   experiments](/doc/start/experiments), having [built-in
   mechanisms](/doc/user-guide/experiment-management) like the
   [run-cache](/doc/user-guide/project-structure/internal-files#run-cache) and
-  the `dvc experiments` commands (available on DVC 2.0 and above).
+  the [`dvc experiments`](/doc/command-reference/exp) commands (available on DVC
+  2.0 and above).
 ---

content/docs/user-guide/experiment-management/index.md

@@ -1,34 +1,38 @@
 # Experiment Management
 
-_New in DVC 2.0_
-
 Data science and ML are iterative processes that require a large number of
 attempts to reach a certain level of a metric. Experimentation is part of the
 development of data features, hyperspace exploration, deep learning
-optimization, etc. DVC helps you codify and manage all of your
-<abbr>experiments</abbr>, supporting these main approaches:
+optimization, etc.
 
-1. Create [experiments](#experiments) that derive from your latest project
-   version without having to track them manually. DVC does that automatically,
-   letting you list and compare them. The best ones can be made persistent, and
-   the rest archived.
-2. Place in-code [checkpoints](#checkpoints-in-source-code) that mark a series
-   of variations, forming a deep experiment. DVC helps you capture them at
-   runtime, and manage them in batches.
-3. Make experiments or checkpoints [persistent](#persistent-experiments) by
-   committing them to your <abbr>repository</abbr>. Or create these versions
-   from scratch like typical project changes.
-
-   At this point you may also want to consider the different
-   [ways to organize](#organization-patterns) experiments in your project (as
-   Git branches, as folders, etc.).
-
-DVC also provides specialized features to codify and analyze experiments.
+Some of DVC's base features already help you codify and analyze experiments.
 [Parameters](/doc/command-reference/params) are simple values you can tweak in a
-human-readable text file, which cause different behaviors in your code and
-models. On the other end, [metrics](/doc/command-reference/metrics) (and
+formatted text file; They cause different behaviors in your code and models. On
+the other end, [metrics](/doc/command-reference/metrics) (and
 [plots](/doc/command-reference/plots)) let you define, visualize, and compare
-meaningful measures for the experimental results.
+quantitative measures of your results.
+
+## DVC Experiments
+
+_New in DVC 2.0_
+
+<abbr>DVC experiments</abbr> support these main approaches:
+
+1. Create [experiments](#experiments) that derive from your latest project
+   version without polluting your Git history. DVC tracks them for you, letting
+   you list and compare them. The best ones can be made persistent, and the rest
+   left as history or cleared.
+1. Queue and process series of experiments based on a parameter search or other
+   modifications to your baseline.
+1. Generate [checkpoints](#checkpoints-in-source-code) during your code
+   execution to analyze the internal progress of deep experiments. DVC captures
+   them at runtime, and can manage them in batches.
+1. Make experiments [persistent](#persistent-experiments) by committing them to
+   your <abbr>repository</abbr> history.
 
 > 👨‍💻 See [Get Started: Experiments](/doc/start/experiments) for a hands-on
 > introduction to DVC experiments.
+
+You may also want to consider the different
+[ways to organize experiments](/doc/user-guide/experiment-management/organization)
+in your project (as Git branches, as folders, etc.).