guide: Exps workflow

content/docs/command-reference/exp/index.md

@@ -2,7 +2,7 @@
 
 ⚠️ This feature is only available in DVC 2.0 ⚠️
 
-A set of commands to generate and manage <abbr>experiment</abbr>:
+A set of commands to generate and manage <abbr>experiments</abbr>:
 [run](/doc/command-reference/exp/run), [show](/doc/command-reference/exp/show),
 [diff](/doc/command-reference/exp/diff),
 [apply](/doc/command-reference/exp/apply),
@@ -38,26 +38,14 @@ positional arguments:
 
 ## Description
 
-`dvc exp` subcommands provide specialized ways to create and manage data science
-experiments. The basic workflow goes like this:
-
-- Modify stage <abbr>parameters</abbr> or other dependencies (e.g. input data,
-  source code) of committed stages.
-- Use `dvc exp run` (instead of `repro`) to execute the pipeline. The results
-  are reflected in your <abbr>workspace</abbr>, and tracked automatically.
-- Use [metrics](/doc/command-reference/metrics) in your pipeline to identify the
-  best experiment(s).
-- Visualize and compare experiments with `dvc exp show` or `dvc exp diff`.
-  Repeat.
-- Use `dvc exp apply` to roll back to the best one.
-- Make the selected experiment persistent by committing its results to Git. This
-  cleans the slate so you can repeat the process.
+`dvc exp` subcommands provide specialized ways to create and manage data
+science/ machine learning experiments.
 
 📖 See [Experiment Management](/doc/user-guide/experiment-management) for more
 info.
 
-> Note that DVC assumes that <abbr>experiments</abbr> are deterministic (see
-> **Avoiding unexpected behavior** in `dvc run`).
+> ⚠️ Note that DVC assumes that experiments are deterministic (see **Avoiding
+> unexpected behavior** in `dvc stage add`).
 
 ## Options
 

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

@@ -28,6 +28,9 @@ 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.
 
+> 👨‍💻 See [Get Started: Experiments](/doc/start/experiments) for a hands-on
+> introduction to DVC experiments.
+
 ## Experiments
 
 ⚠️ This feature is only available in DVC 2.0 ⚠️
@@ -43,8 +46,8 @@ roll back to the baseline. The basic workflow goes like this:
   are reflected in your <abbr>workspace</abbr>, and tracked automatically.
 - Use [metrics](/doc/command-reference/metrics) to identify the best
   experiment(s).
-- Visualize and compare experiments with `dvc exp show` or `dvc exp diff`.
-  Repeat.
+- Visualize, compare experiments with `dvc exp show` or `dvc exp diff`. Repeat
+  🔄
 - Use `dvc exp apply` to roll back to the best one.
 - Make the selected experiment persistent by committing its results to Git. This
   cleans the slate so you can repeat the process.
@@ -74,9 +77,6 @@ version. The right <abbr>outputs</abbr> (including
 [metrics](/doc/command-reference/metrics)) should also be present, or available
 via `dvc checkout`.
 
-> 👨‍💻 See [Get Started: Experiments](/doc/start/experiments) for a hands-on
-> introduction to regular experiments.
-
 ### Organization patterns
 
 DVC takes care of arranging `dvc exp` experiments and the data