diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -36,23 +36,149 @@ If you find any issues installing the package, please do not hesitate to [open a
 
 ### 1.2 Describing instances
 
+```julia
+using JuMP
+using MIPLearn
+
+# Create problem data
+weights = [1.0, 2.0, 3.0]
+prices = [5.0, 6.0, 7.0]
+capacity = 3.0
+
+# Create standard JuMP model
+model = Model()
+n = length(weights)
+@variable(model, x[1:n], Bin)
+@objective(model, Max, sum(x[i] * prices[i] for i in 1:n))
+@constraint(model, c1, sum(x[i] * weights[i] for i in 1:n) <= capacity)
+
+# Add ML information
+@feature(model, [5.0])
+@feature(c1, [1.0, 2.0, 3.0])
+@category(c1, "c1")
+for i in 1:n
+    @feature(x[i], [weights[i]; prices[i]])
+    @category(x[i], "type-$i")
+end
+
+instance = JuMPInstance(model)
+```
 
 ### 1.3 Solving instances and training
 
+
+```julia
+using MIPLearn
+using Cbc
+
+# Create training and test instances
+training_instances = [...]
+test_instances = [...]
+
+# Create solver
+solver = LearningSolver(Cbc.Optimizer)
+
+# Solve training instances
+for instance in train_instances
+    solve!(solver, instance)
+end
+
+# Train ML models
+fit!(solver, training_instances)
+
+# Solve test instances
+for instance in test_instances
+    solve!(solver, instance)
+end
+```
+
 ### 1.4 Saving and loading solver state
+```julia
+using MIPLearn
+using Cbc
+
+# Solve training instances
+training_instances = [...]
+solver = LearningSolver(Cbc.Optimizer)
+for instance in training_instances
+    solve!(solver, instance)
+end
+
+# Train ML models
+fit!(solver, training_instances)
+
+# Save trained solver to disk
+save!(solver, "solver.bin")
+
+# Application restarts...
+
+# Load trained solver from disk
+solver = LearningSolver(Cbc.Optimizer)
+load!(solver, "solver.bin")
+
+# Solve additional instances
+test_instances = [...]
+for instance in test_instances
+    solve!(solver, instance)
+end
+
+```
 
 ### 1.5 Solving training instances in parallel
+```julia
+using MIPLearn
+using Cbc
+
+# Solve training instances in parallel
+training_instances = [...]
+solver = LearningSolver(Cbc.Optimizer)
+parallel_solve!(solver, training_instances, n_jobs=4)
+fit!(solver, training_instances)
+
+# Solve test instances in parallel
+test_instances = [...]
+parallel_solve!(solver, test_instances)
+```
 
 ## 2. Customization
 
 ### 2.1 Selecting solver components
 
+```julia
+using MIPLearn
+
+solver = LearningSolver(
+  Cbc.Optimizer,
+  components=[
+    PrimalSolutionComponent(...),
+    ObjectiveValueComponent(...),
+  ]
+)
+```
+
 ### 2.2 Adjusting component aggresiveness
 
+```julia
+using MIPLearn
+
+solver = LearningSolver(
+  Cbc.Optimizer,
+  components=[
+    PrimalSolutionComponent(
+      threshold=MinPrecisionThreshold(0.95),
+    ),
+  ]
+)
+```
+
 ### 2.3 Evaluating component performance
 
+TODO
+
 ### 2.4 Using customized ML classifiers and regressors
 
+TODO
+
 ## 3. Acknowledgments
 * Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.
 * Based upon work supported by the **U.S. Department of Energy Advanced Grid Modeling Program** under Grant DE-OE0000875.