diff --git a/docs/src/interfaces/Init_Solve.md b/docs/src/interfaces/Init_Solve.md
index 840749c7f..66cd2b524 100644
--- a/docs/src/interfaces/Init_Solve.md
+++ b/docs/src/interfaces/Init_Solve.md
@@ -37,7 +37,7 @@ is distinctly different from the
 [LinearSolve init interface](https://docs.sciml.ai/LinearSolve/stable/tutorials/caching_interface)
 which is designed for caching efficiency with reusing factorizations.
 
-## __solve and High-Level Handling
+## `__solve` and High-Level Handling
 
 While `init` and `solve` are the common entry point for users, solver packages will
 mostly define dispatches on `SciMLBase.__init` and `SciMLBase.__solve`. The reason is
diff --git a/docs/src/interfaces/Problems.md b/docs/src/interfaces/Problems.md
index 3423d5a2a..db253e41a 100644
--- a/docs/src/interfaces/Problems.md
+++ b/docs/src/interfaces/Problems.md
@@ -86,7 +86,7 @@ usage, a `AbstractSciMLProblem` might be associated with some solver configurati
 callback or tolerance. Thus, for flexibility the extra keyword arguments to the
 `AbstractSciMLProblem` are carried to the solver.
 
-### problem_type
+### `problem_type`
 
 `AbstractSciMLProblem` types include a non-public API definition of `problem_type` which holds
 a trait type corresponding to the way the `AbstractSciMLProblem` was constructed. For example,
diff --git a/docs/src/interfaces/SciMLFunctions.md b/docs/src/interfaces/SciMLFunctions.md
index 9f062bff7..8cad66f73 100644
--- a/docs/src/interfaces/SciMLFunctions.md
+++ b/docs/src/interfaces/SciMLFunctions.md
@@ -94,8 +94,8 @@ on setting up time/parameter dependent operators.
 The solver libraries internally use packages such as [FiniteDiff.jl](https://docs.sciml.ai/FiniteDiff/stable/)
 and [SparseDiffTools.jl](https://docs.sciml.ai/SparseDiffTools/stable/) for
 high performance calculation of sparse Jacobians and Hessians, along with matrix-free
-calculations of Jacobian-Vector products (J*v), vector-Jacobian products (v'*J),
-and Hessian-vector products (H*v). The SciML interface gives users the ability
+calculations of Jacobian-Vector products (`J*v`), vector-Jacobian products (`v'*J`),
+and Hessian-vector products (`H*v`). The SciML interface gives users the ability
 to control these connections in order to allow for top notch performance.
 
 The key arguments in the SciMLFunction is the `prototype`, which is an object
diff --git a/src/SciMLBase.jl b/src/SciMLBase.jl
index 595d0be35..9a021fceb 100644
--- a/src/SciMLBase.jl
+++ b/src/SciMLBase.jl
@@ -100,7 +100,7 @@ abstract type AbstractOptimizationCache end
 """
 $(TYPEDEF)
 
-Base for types which define nonlinear solve problems (f(u)=0).
+Base for types which define nonlinear solve problems (`f(u)=0`).
 """
 abstract type AbstractNonlinearProblem{uType, isinplace} <: AbstractDEProblem end
 abstract type AbstractIntervalNonlinearProblem{uType, isinplace} <:
diff --git a/src/alg_traits.jl b/src/alg_traits.jl
index 9f1b94246..0b86ad67a 100644
--- a/src/alg_traits.jl
+++ b/src/alg_traits.jl
@@ -1,5 +1,5 @@
 """
-isautodifferentiable(alg::AbstractDEAlgorithm)
+    isautodifferentiable(alg::AbstractDEAlgorithm)
 
 Trait declaration for whether an algorithm is compatible with
 direct automatic differentiation, i.e. can have algorithms like
@@ -11,7 +11,7 @@ Defaults to false as only pure-Julia algorithms can have this be true.
 isautodifferentiable(alg::AbstractSciMLAlgorithm) = false
 
 """
-forwarddiffs_model(alg::AbstractDEAlgorithm)
+    forwarddiffs_model(alg::AbstractDEAlgorithm)
 
 Trait declaration for whether an algorithm uses ForwardDiff.jl
 on the model function is called with ForwardDiff.jl
@@ -21,7 +21,7 @@ Defaults to false as only pure-Julia algorithms can have this be true.
 forwarddiffs_model(alg::AbstractSciMLAlgorithm) = false
 
 """
-forwarddiffs_model_time(alg::AbstractDEAlgorithm)
+    forwarddiffs_model_time(alg::AbstractDEAlgorithm)
 
 Trait declaration for whether an algorithm uses ForwardDiff.jl
 on the model `f(u,p,t)` function is called with ForwardDiff.jl on the `t` argument.
@@ -32,7 +32,7 @@ have this as true
 forwarddiffs_model_time(alg::AbstractSciMLAlgorithm) = false
 
 """
-allows_arbitrary_number_types(alg::AbstractDEAlgorithm)
+    allows_arbitrary_number_types(alg::AbstractDEAlgorithm)
 
 Trait declaration for whether an algorithm is compatible with
 direct automatic differentiation, i.e. can have algorithms like
@@ -44,7 +44,7 @@ Defaults to false as only pure-Julia algorithms can have this be true.
 allows_arbitrary_number_types(alg::AbstractSciMLAlgorithm) = false
 
 """
-allowscomplex(alg::AbstractDEAlgorithm)
+    allowscomplex(alg::AbstractDEAlgorithm)
 
 Trait declaration for whether an algorithm is compatible with
 having complex numbers as the state variables.
@@ -54,7 +54,7 @@ Defaults to false.
 allowscomplex(alg::AbstractSciMLAlgorithm) = false
 
 """
-isadaptive(alg::AbstractDEAlgorithm)
+    isadaptive(alg::AbstractDEAlgorithm)
 
 Trait declaration for whether an algorithm uses adaptivity,
 i.e. has a non-quasi-static compute graph.
@@ -65,7 +65,7 @@ isadaptive(alg::AbstractDEAlgorithm) = true
 # Default to assuming adaptive, safer error("Adaptivity algorithm trait not set.")
 
 """
-isdiscrete(alg::AbstractDEAlgorithm)
+    isdiscrete(alg::AbstractDEAlgorithm)
 
 Trait declaration for whether an algorithm allows for
 discrete state values, such as integers.
@@ -75,7 +75,7 @@ Defaults to false.
 isdiscrete(alg::AbstractDEAlgorithm) = false
 
 """
-allowsbounds(opt)
+    allowsbounds(opt)
 
 Trait declaration for whether an optimizer allows for
 box constraints passed with `lb` and `ub` in
@@ -86,7 +86,7 @@ Defaults to false.
 allowsbounds(opt) = false
 
 """
-requiresbounds(opt)
+    requiresbounds(opt)
 
 Trait declaration for whether an optimizer requires
 box constraints passed with `lb` and `ub` in
@@ -97,7 +97,7 @@ Defaults to false.
 requiresbounds(opt) = false
 
 """
-allowsconstraints(opt)
+    allowsconstraints(opt)
 
 Trait declaration for whether an optimizer allows
 non-linear constraints specified in `cons` in
@@ -108,7 +108,7 @@ Defaults to false.
 allowsconstraints(opt) = false
 
 """
-requiresconstraints(opt)
+    requiresconstraints(opt)
 
 Trait declaration for whether an optimizer
 requires non-linear constraints specified in
@@ -119,7 +119,7 @@ Defaults to false.
 requiresconstraints(opt) = false
 
 """
-requiresgradient(opt)
+    requiresgradient(opt)
 
 Trait declaration for whether an optimizer
 requires gradient in `instantiate_function`.
@@ -129,7 +129,7 @@ Defaults to false.
 requiresgradient(opt) = false
 
 """
-requireshessian(opt)
+    requireshessian(opt)
 
 Trait declaration for whether an optimizer
 requires hessian in `instantiate_function`.
@@ -139,17 +139,17 @@ Defaults to false.
 requireshessian(opt) = false
 
 """
-requiresconsjac(opt)
+    requiresconsjac(opt)
 
 Trait declaration for whether an optimizer
-requires cons_j in `instantiate_function`, that is, does the optimizer require a constant Jacobian.
+requires `cons_j` in `instantiate_function`, that is, does the optimizer require a constant Jacobian.
 
 Defaults to false.
 """
 requiresconsjac(opt) = false
 
 """
-requiresconshess(opt)
+    requiresconshess(opt)
 
 Trait declaration for whether an optimizer
 requires cons_h in `instantiate_function`, that is, does the optimizer require a constant hessian.
@@ -159,7 +159,7 @@ Defaults to false.
 requiresconshess(opt) = false
 
 """
-allowscallback(opt)
+    allowscallback(opt)
 
 Trait declaration for whether an optimizer
 supports passing a `callback` to `solve`
@@ -170,7 +170,7 @@ Defaults to true.
 allowscallback(opt) = true
 
 """
-alg_order(alg)
+    alg_order(alg)
 
 The theoretic convergence order of the algorithm. If the method is adaptive order, this is treated
 as the maximum order of the algorithm.
diff --git a/src/debug.jl b/src/debug.jl
index a9f7634d6..9cb5c5928 100644
--- a/src/debug.jl
+++ b/src/debug.jl
@@ -49,11 +49,11 @@ into the AbstractSciMLProblem (e.x.: ODEProblem) but the parameters object `p` w
 expression. Two common reasons for this issue are:
 
 1. Forgetting to pass parameters into the problem constructor. For example, `ODEProblem(f,u0,tspan)` should
-be `ODEProblem(f,u0,tspan,p)` in order to use parameters.
+   be `ODEProblem(f,u0,tspan,p)` in order to use parameters.
 
 2. Using the wrong function signature. For example, with `ODEProblem`s the function signature is always
-`f(du,u,p,t)` for the in-place form or `f(u,p,t)` for the out-of-place form. Note that the `p` argument
-will always be in the function signature regardless of if the problem is defined with parameters!
+   `f(du,u,p,t)` for the in-place form or `f(u,p,t)` for the out-of-place form. Note that the `p` argument
+   will always be in the function signature regardless of if the problem is defined with parameters!
 """
 
 function __init__()
diff --git a/src/integrator_interface.jl b/src/integrator_interface.jl
index eec89165b..6970b44d0 100644
--- a/src/integrator_interface.jl
+++ b/src/integrator_interface.jl
@@ -283,7 +283,7 @@ function reinit!(integrator::DEIntegrator, args...; kwargs...)
 end
 
 """
-initialize_dae!(integrator::DEIntegrator,initializealg = integrator.initializealg)
+    initialize_dae!(integrator::DEIntegrator,initializealg = integrator.initializealg)
 
 Runs the DAE initialization to find a consistent state vector. The optional
 argument `initializealg` can be used to specify a different initialization
@@ -927,7 +927,7 @@ end
 has_stats(i::DEIntegrator) = false
 
 """
-    is_integrator_adaptive(i::DEIntegrator)
+    isadaptive(i::DEIntegrator)
 
 Checks if the integrator is adaptive
 """
diff --git a/src/operators/diffeq_operator.jl b/src/operators/diffeq_operator.jl
index b62c2bc5b..d31ebec3e 100644
--- a/src/operators/diffeq_operator.jl
+++ b/src/operators/diffeq_operator.jl
@@ -1,5 +1,5 @@
 """
-AffineDiffEqOperator{T} <: AbstractDiffEqOperator{T}
+    AffineDiffEqOperator{T} <: AbstractDiffEqOperator{T}
 
 `Ex: (A₁(t) + ... + Aₙ(t))*u + B₁(t) + ... + Bₘ(t)`
 
diff --git a/src/problems/problem_utils.jl b/src/problems/problem_utils.jl
index 8eaa6f8bf..15462e8b1 100644
--- a/src/problems/problem_utils.jl
+++ b/src/problems/problem_utils.jl
@@ -149,11 +149,11 @@ into the AbstractSciMLProblem (e.x.: ODEProblem) but the parameters object `p` w
 expression (e.x. `p[i]`, or `x .+ p`). Two common reasons for this issue are:
 
 1. Forgetting to pass parameters into the problem constructor. For example, `ODEProblem(f,u0,tspan)` should
-be `ODEProblem(f,u0,tspan,p)` in order to use parameters.
+   be `ODEProblem(f,u0,tspan,p)` in order to use parameters.
 
 2. Using the wrong function signature. For example, with `ODEProblem`s the function signature is always
-`f(du,u,p,t)` for the in-place form or `f(u,p,t)` for the out-of-place form. Note that the `p` argument
-will always be in the function signature regardless of if the problem is defined with parameters!
+   `f(du,u,p,t)` for the in-place form or `f(u,p,t)` for the out-of-place form. Note that the `p` argument
+   will always be in the function signature regardless of if the problem is defined with parameters!
 """
 
 struct NullParameterIndexError <: Exception end
diff --git a/src/retcodes.jl b/src/retcodes.jl
index eacc71341..0910331b5 100644
--- a/src/retcodes.jl
+++ b/src/retcodes.jl
@@ -1,5 +1,5 @@
 """
-`SciML.ReturnCode`
+    SciML.ReturnCode
 
 `SciML.ReturnCode` is the standard return code enum interface for the SciML interface.
 Return codes are notes given by the solvers to indicate the state of the solution, for
@@ -31,7 +31,7 @@ did not error.
 """
 EnumX.@enumx ReturnCode begin
     """
-    `ReturnCode.Default`
+        ReturnCode.Default
 
     The default state of the solver. If this return code is given, then the solving
     process is either still in process or the solver library has not been setup
@@ -49,12 +49,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     Default
 
     """
-    `ReturnCode.Success`
+        ReturnCode.Success
 
     The success state of the solver. If this return code is given, then the solving
     process was successful, but no extra information about that success is given.
@@ -66,12 +66,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = true
+      - `successful_retcode` = `true`
     """
     Success
 
     """
-    `ReturnCode.Terminated`
+        ReturnCode.Terminated
 
     The successful termination state of the solver. If this return code is given,
     then the solving process was successful at terminating the solve, usually
@@ -90,12 +90,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = true
+      - `successful_retcode` = `true`
     """
     Terminated
 
     """
-    `ReturnCode.DtNaN`
+        ReturnCode.DtNaN
 
     A failure exit state of the solver. If this return code is given, then the
     solving process was unsuccessful and exited early because the `dt` of the
@@ -113,12 +113,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     DtNaN
 
     """
-    `ReturnCode.MaxIters`
+        ReturnCode.MaxIters
 
     A failure exit state of the solver. If this return code is given, then the
     solving process was unsuccessful and exited early because the solver's
@@ -145,12 +145,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     MaxIters
 
     """
-    `ReturnCode.DtLessThanMin`
+        ReturnCode.DtLessThanMin
 
     A failure exit state of the solver. If this return code is given, then the
     solving process was unsuccessful and exited early because the `dt` of the
@@ -175,12 +175,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     DtLessThanMin
 
     """
-    `ReturnCode.Unstable`
+        ReturnCode.Unstable
 
     A failure exit state of the solver. If this return code is given, then the
     solving process was unsuccessful and exited early because the `unstable_check`
@@ -194,12 +194,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     Unstable
 
     """
-    `ReturnCode.InitialFailure`
+        ReturnCode.InitialFailure
 
     A failure exit state of the solver. If this return code is given, then the
     solving process was unsuccessful because the initialization process failed.
@@ -219,12 +219,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     InitialFailure
 
     """
-    `ReturnCode.ConvergenceFailure`
+        ReturnCode.ConvergenceFailure
 
     A failure exit state of the solver. If this return code is given, then the
     solving process was unsuccessful because internal nonlinear solver iterations
@@ -242,12 +242,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     ConvergenceFailure
 
     """
-    `ReturnCode.Failure`
+        ReturnCode.Failure
 
     A failure exit state of the solver. If this return code is given, then the
     solving process was unsuccessful but no extra information is given.
@@ -260,12 +260,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     Failure
 
     """
-    `ReturnCode.ExactSolutionLeft`
+        ReturnCode.ExactSolutionLeft
 
     The success state of the solver. If this return code is given, then the solving
     process was successful, and the left solution was given.
@@ -279,12 +279,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = true
+      - `successful_retcode` = `true`
     """
     ExactSolutionLeft
 
     """
-    `ReturnCode.ExactSolutionRight`
+        ReturnCode.ExactSolutionRight
 
     The success state of the solver. If this return code is given, then the solving
     process was successful, and the right solution was given.
@@ -298,12 +298,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = true
+      - `successful_retcode` = `true`
     """
     ExactSolutionRight
 
     """
-    `ReturnCode.FloatingPointLimit`
+        ReturnCode.FloatingPointLimit
 
     The success state of the solver. If this return code is given, then the solving
     process was successful, and the closest floating point value to the solution was given.
@@ -317,23 +317,23 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = true
+      - `successful_retcode` = `true`
     """
     FloatingPointLimit
 
     """
-    `ReturnCode.Infeasible`
+        ReturnCode.Infeasible
 
     The optimization problem was proven to be infeasible by the solver.
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     Infeasible
 
     """
-    `ReturnCode.MaxTime`
+        ReturnCode.MaxTime
 
     A failure exit state of the solver. If this return code is given, then the
     solving process was unsuccessful and exited early because the solver's
@@ -342,34 +342,34 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     MaxTime
 
     """
-    `ReturnCode.InternalLineSearchFailed`
+        ReturnCode.InternalLineSearchFailed
 
     Internal Line Search used by the algorithm has failed.
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     InternalLineSearchFailed
 
     """
-    `ReturnCode.ShrinkThresholdExceeded`
+        ReturnCode.ShrinkThresholdExceeded
 
     The trust region radius was shrunk more times than the provided threshold.
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     ShrinkThresholdExceeded
 
     """
-    `ReturnCode.Stalled`
+        ReturnCode.Stalled
 
     The solution has stalled. This is only returned by algorithms for which stalling is a
     failure mode. Certain solvers like Nonlinear Least Squares solvers are considered
@@ -377,12 +377,12 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     Stalled
 
     """
-    `ReturnCode.InternalLinearSolveFailed`
+        ReturnCode.InternalLinearSolveFailed
 
     The linear problem inside another problem (for example inside a NonlinearProblem)
     could not be solved.
@@ -394,7 +394,7 @@ EnumX.@enumx ReturnCode begin
 
     ## Properties
 
-      - successful_retcode = false
+      - `successful_retcode` = `false`
     """
     InternalLinearSolveFailed
 end
@@ -477,8 +477,8 @@ function Base.convert(::Type{ReturnCode.T}, bool::Bool)
 end
 
 """
-`successful_retcode(retcode::ReturnCode.T)::Bool`
-`successful_retcode(sol::AbstractSciMLSolution)::Bool`
+    successful_retcode(retcode::ReturnCode.T)::Bool
+    successful_retcode(sol::AbstractSciMLSolution)::Bool
 
 Returns a boolean for whether a return code should be interpreted as a form of success.
 """
diff --git a/src/scimlfunctions.jl b/src/scimlfunctions.jl
index becff1bf9..a89e2c9d6 100644
--- a/src/scimlfunctions.jl
+++ b/src/scimlfunctions.jl
@@ -70,7 +70,7 @@ Cases where automatic wrapping is disabled are equivalent to `FullSpecialize`.
 
 ## Example
 
-```
+```julia
 f(du,u,p,t) = (du .= u)
 
 # Note this is the same as ODEProblem(f, [1.0], (0.0,1.0))
@@ -93,7 +93,7 @@ time. Unlike `AutoSpecialize`, `NoSpecialize` can be used with any
 
 ## Example
 
-```
+```julia
 f(du,u,p,t) = (du .= u)
 ODEProblem{true, SciMLBase.NoSpecialize}(f, [1.0], (0.0,1.0))
 ```
@@ -133,7 +133,7 @@ but also includes the limitations:
 
 ## Example
 
-```
+```julia
 f(du,u,p,t) = (du .= u)
 ODEProblem{true, SciMLBase.FunctionWrapperSpecialize}(f, [1.0], (0.0,1.0))
 ```
@@ -154,7 +154,7 @@ is required, such as in long-running simulations and benchmarking.
 
 ## Example
 
-```
+```julia
 f(du,u,p,t) = (du .= u)
 ODEProblem{true, SciMLBase.FullSpecialize}(f, [1.0], (0.0,1.0))
 ```