diff --git a/docs/Project.toml b/docs/Project.toml
index c49adf0..28bdf0d 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -1,4 +1,5 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
+LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
 OMOPCDMCohortCreator = "f525a15e-a73f-4eef-870f-f901257eae22"
diff --git a/docs/src/tutorials/custom_functions.md b/docs/src/tutorials/custom_functions.md
new file mode 100644
index 0000000..739da70
--- /dev/null
+++ b/docs/src/tutorials/custom_functions.md
@@ -0,0 +1,193 @@
+# Beginner Tutorial 🐣
+
+```@contents
+Pages = ["custom_functions.md"]
+```
+
+This tutorial presents a step by step guide on using OMOPCDMCohortCreator to run a mini characterization study!
+You will learn the basics of OMOPCDMCohortCreator and how to use it with fake data that you could then apply to your real data sets.
+Basic knowledge of Julia (such as installing packages into environments and working with the Julia REPL and Julia files) is necessary; you can learn all [that here](https://pkgdocs.julialang.org/v1/getting-started/).
+
+## Environment Set-Up 📝
+
+For this tutorial, you will need to activate an environment; to get into package mode within your Julia REPL, write `]`:
+
+```julia-repl
+pkg> activate TUTORIAL
+```
+
+### Packages 
+
+You will need the following packages for this tutorial which you can install in package mode:
+
+```julia-repl
+TUTORIAL> add OMOPCDMCohortCreator
+TUTORIAL> add SQLite
+TUTORIAL> add DataFrames
+TURORIAL> add HealthSampleData
+```
+
+To learn more about these packages, see the [Appendix](#appendix).
+
+### Data 
+
+For this tutorial, we will work with data from [Eunomia](https://github.com/OHDSI/Eunomia) that is stored in a SQLite format.
+To install the data on your machine, execute the following code block and follow the prompts - you will need a stable internet connection for the download to complete: 
+
+```julia
+import HealthSampleData: Eunomia
+
+eunomia = Eunomia()
+```
+
+## Connecting to the Eunomia Database 💾
+
+After you have finished your set up in the Julia, we need to establish a connection to the Eunomia SQLite database that we will use for the rest of the tutorial: 
+
+```julia
+import SQLite: DB
+
+conn = DB(eunomia)
+```
+
+With Eunomia, the database's schema is simply called "main".
+We will use this to generate database connection details that will inform `OMOPCDMCohortCreator` about the type of queries we will write (i.e. SQLite) and the name of the database's schema.
+For this step, we will use `OMOPCDMCohortCreator`:
+
+```julia
+import OMOPCDMCohortCreator as occ
+
+occ.GenerateDatabaseDetails(
+    :sqlite,
+    "main"
+)
+```
+
+Finally, we will generate internal representations of each table found within Eunomia for OMOPCDMCohortCreator to use:
+
+```julia
+occ.GenerateTables(conn)
+```
+
+As a check to make sure everything was correctly installed and works properly, the following block should work and return a list of all person ids in this data:
+
+```julia
+occ.GetDatabasePersonIDs(conn)
+```
+
+## Characterizing Patients Who Have Had Strep Throat 🤒
+
+### Background 
+
+### Details
+
+how can i execute sql statements against generated tables i had done
+stmt = DBInterface.prepare(conn, """SELECT * FROM  omop.procedure_occurrence  LIMIT 1""") # prepare a sql statement against the connection; returns a statement object
+results = DBInterface.execute(stmt) # execute a prepared statement; returns an iterator of rows (property-accessible & indexable)
+result.column_names
+but it prints just
+2-element Vector{String}:
+ "no_nulls"
+ "yes_nulls"
+
+
+Jakub Mitura
+  1 day ago
+I had looked into your code and you seem to use FunSQL which seem really nice library however when i try to replicate your code I can not get reference to table
+code:
+using FunSQL: From,Limit,render
+
+conn = connect(
+    Connection, 
+    "host=localhost port=5432 dbname=synthea user=thecedarprince password=pass*"
+)    
+GenerateDatabaseDetails(:postgresql, "omop")
+GenerateTables(conn)
+
+q = From(:procedure_occurrence)|>
+Limit(1) |>
+q -> render(q, dialect=OMOPCDMCohortCreator.dialect)
+although during initialization I get
+[ Info: procedure_occurrence table generated internally
+I get error
+ FunSQL.ReferenceError: cannot find `procedure_occurrence`
+Thanks for patience !
+Saved for later • Due 24 hours ago
+
+
+Jakub Mitura
+  1 day ago
+@TheCedarPrince
+ the funsql way seem very nice just do not have idea how to access tables that during initialization logging info stated it was created
+
+
+TheCedarPrince
+:deciduous_tree:  1 day ago
+Hey 
+@Jakub Mitura
+ -- I'll write a tutorial and add that it into the documentation but the short answer to this is as follows.
+:thumbsup_all:
+1
+
+
+
+TheCedarPrince
+:deciduous_tree:  1 day ago
+using OMOPCDMCohortCreator
+
+# Create connection here
+# Other code
+GenerateDatabaseDetails(:postgresql, "omop")
+tables = GenerateTables(conn, inplace = false, exported = true)
+Then, within that tables variable, you can get FunSQL representations of the tables you want:
+person = tables[:person]
+:thumbsup_all:
+1
+
+
+
+TheCedarPrince
+:deciduous_tree:  1 day ago
+Then you can write queries such as:
+sql = From(person) |> Select(Get.person_id) |> x -> render(x, :postgresql)
+DBInterface.execute(conn, sql) |> DataFrame
+:thumbsup_all:
+1
+
+
+
+TheCedarPrince
+:deciduous_tree:  1 day ago
+Hopefully that should get you unstuck 
+@Jakub Mitura
+ and sorry for the delay in response.
+:thumbsup_all:
+1
+
+
+
+TheCedarPrince
+:deciduous_tree:  1 day ago
+Additionally, the FunSQL reference documentation is fantastic here: https://mechanicalrabbit.github.io/FunSQL.jl/dev/guide/
+
+## Conclusion 🎉
+
+This mini characterization study that we just conducted on this dataset opens up a whole new avenue for a researcher to pursue.
+For example, we could now calculate prevalence rates across different patient characteristics or compare and contrast multiple conditions at once.
+It should also be apparent that the API is set up in a very particular way: it is functional meaning that each function does one thing only.
+This gives a lot of flexibility to a user to build together study incrementally using OMOPCDMCohortCreator.
+Congratulations on finishing this tutorial and if there are any issues you encountered, [feel free to open an issue here](https://github.com/JuliaHealth/OMOPCDMCohortCreator.jl/issues/new/choose)!
+
+## Appendix 🕵️
+
+### Packages Used in Analysis
+
+Package descriptions:
+
+- [`DataFrames`](https://github.com/JuliaData/DataFrames.jl) - Julia's dataframe handler for easily manipulating data
+
+- [`OMOPCDMCohortCreator`](https://github.com/JuliaHealth/OMOPCDMCohortCreator.jl) - Create cohorts from databases utilizing the OMOP CDM
+
+- [`HealthSampleData`](https://github.com/JuliaHealth/HealthSampleData.jl) - Sample health data for a variety of health formats and use cases
+
+- [`SQLite`](https://github.com/JuliaDatabases/SQLite.jl) - A Julia interface to the SQLite library
diff --git a/src/filters.jl b/src/filters.jl
index 307bda9..a0fc565 100644
--- a/src/filters.jl
+++ b/src/filters.jl
@@ -282,4 +282,31 @@ function StateFilterPersonIDs(states; tab = location, join_tab = person)
 
 end
 
-export VisitFilterPersonIDs, ConditionFilterPersonIDs, RaceFilterPersonIDs, GenderFilterPersonIDs, StateFilterPersonIDs
\ No newline at end of file
+"""
+TODO: Update these docstrings
+"""
+function DrugExposureFilterPersonIDs(
+    drug_concept_ids, conn;
+    tab = drug_exposure,
+)
+    df = DBInterface.execute(conn, DrugExposureFilterPersonIDs(drug_concept_ids; tab = tab)) |> DataFrame
+
+    return df 
+
+end
+
+function DrugExposureFilterPersonIDs(
+    drug_concept_ids;
+    tab = drug_exposure,
+)
+    sql =
+        From(tab) |>
+        Where(Fun.in(Get.drug_concept_id, drug_concept_ids...)) |>
+        Group(Get.person_id) |>
+        q -> render(q, dialect = dialect)
+
+        return String(sql)
+
+end
+
+export VisitFilterPersonIDs, ConditionFilterPersonIDs, RaceFilterPersonIDs, GenderFilterPersonIDs, StateFilterPersonIDs