diff --git a/app/client/components/DocComm.ts b/app/client/components/DocComm.ts
index fc211c24..1a0dfe37 100644
--- a/app/client/components/DocComm.ts
+++ b/app/client/components/DocComm.ts
@@ -32,6 +32,7 @@ export class DocComm extends Disposable implements ActiveDocAPI {
   public addAttachments = this._wrapMethod("addAttachments");
   public findColFromValues = this._wrapMethod("findColFromValues");
   public getFormulaError = this._wrapMethod("getFormulaError");
+  public getAssistance = this._wrapMethod("getAssistance");
   public fetchURL = this._wrapMethod("fetchURL");
   public autocomplete = this._wrapMethod("autocomplete");
   public removeInstanceFromDoc = this._wrapMethod("removeInstanceFromDoc");
diff --git a/app/common/ActiveDocAPI.ts b/app/common/ActiveDocAPI.ts
index 94ad3cd6..e2ae0dcc 100644
--- a/app/common/ActiveDocAPI.ts
+++ b/app/common/ActiveDocAPI.ts
@@ -318,6 +318,11 @@ export interface ActiveDocAPI {
    */
   getFormulaError(tableId: string, colId: string, rowId: number): Promise<CellValue>;
 
+  /**
+   * Generates a formula code based on the AI suggestions, it also modifies the column and sets it type to a formula.
+   */
+  getAssistance(tableId: string, colId: string, description: string): Promise<void>;
+
   /**
    * Fetch content at a url.
    */
diff --git a/app/common/AssistancePrompts.ts b/app/common/AssistancePrompts.ts
new file mode 100644
index 00000000..c0ee269e
--- /dev/null
+++ b/app/common/AssistancePrompts.ts
@@ -0,0 +1,11 @@
+import {DocAction} from 'app/common/DocActions';
+
+export interface Prompt {
+  tableId: string;
+  colId: string
+  description: string;
+}
+
+export interface Suggestion {
+  suggestedActions: DocAction[];
+}
diff --git a/app/server/lib/ActiveDoc.ts b/app/server/lib/ActiveDoc.ts
index d8beb8f3..03b8a61c 100644
--- a/app/server/lib/ActiveDoc.ts
+++ b/app/server/lib/ActiveDoc.ts
@@ -14,6 +14,7 @@ import {
 } from 'app/common/ActionBundle';
 import {ActionGroup, MinimalActionGroup} from 'app/common/ActionGroup';
 import {ActionSummary} from "app/common/ActionSummary";
+import {Prompt, Suggestion} from "app/common/AssistancePrompts";
 import {
   AclResources,
   AclTableDescription,
@@ -80,6 +81,7 @@ import {parseUserAction} from 'app/common/ValueParser';
 import {ParseOptions} from 'app/plugin/FileParserAPI';
 import {AccessTokenOptions, AccessTokenResult, GristDocAPI} from 'app/plugin/GristAPI';
 import {compileAclFormula} from 'app/server/lib/ACLFormula';
+import {sendForCompletion} from 'app/server/lib/Assistance';
 import {Authorizer} from 'app/server/lib/Authorizer';
 import {checksumFile} from 'app/server/lib/checksumFile';
 import {Client} from 'app/server/lib/Client';
@@ -1313,6 +1315,24 @@ export class ActiveDoc extends EventEmitter {
     return this._pyCall('autocomplete', txt, tableId, columnId, rowId, user.toJSON());
   }
 
+  public async getAssistance(docSession: DocSession, userPrompt: Prompt): Promise<Suggestion> {
+    // Making a prompt can leak names of tables and columns.
+    if (!await this._granularAccess.canScanData(docSession)) {
+      throw new Error("Permission denied");
+    }
+    await this.waitForInitialization();
+    const { tableId, colId, description } = userPrompt;
+    const prompt = await this._pyCall('get_formula_prompt', tableId, colId, description);
+    this._log.debug(docSession, 'getAssistance prompt', {prompt});
+    const completion = await sendForCompletion(prompt);
+    this._log.debug(docSession, 'getAssistance completion', {completion});
+    const formula = await this._pyCall('convert_formula_completion', completion);
+    const action: DocAction = ["ModifyColumn", tableId, colId, {formula}];
+    return {
+      suggestedActions: [action],
+    };
+  }
+
   public fetchURL(docSession: DocSession, url: string, options?: FetchUrlOptions): Promise<UploadResult> {
     return fetchURL(url, this.makeAccessId(docSession.authorizer.getUserId()), options);
   }
diff --git a/app/server/lib/Assistance.ts b/app/server/lib/Assistance.ts
new file mode 100644
index 00000000..ce994326
--- /dev/null
+++ b/app/server/lib/Assistance.ts
@@ -0,0 +1,110 @@
+/**
+ * Module with functions used for AI formula assistance.
+ */
+
+import {delay} from 'app/common/delay';
+import log from 'app/server/lib/log';
+import fetch, { Response as FetchResponse} from 'node-fetch';
+
+
+export async function sendForCompletion(prompt: string): Promise<string> {
+  let completion: string|null = null;
+  if (process.env.OPENAI_API_KEY) {
+    completion = await sendForCompletionOpenAI(prompt);
+  }
+  if (process.env.HUGGINGFACE_API_KEY) {
+    completion = await sendForCompletionHuggingFace(prompt);
+  }
+  if (completion === null) {
+    throw new Error("Please set OPENAI_API_KEY or HUGGINGFACE_API_KEY (and optionally COMPLETION_MODEL)");
+  }
+  log.debug(`Received completion:`, {completion});
+  completion = completion.split(/\n {4}[^ ]/)[0];
+  return completion;
+}
+
+
+async function sendForCompletionOpenAI(prompt: string) {
+  const apiKey = process.env.OPENAI_API_KEY;
+  if (!apiKey) {
+    throw new Error("OPENAI_API_KEY not set");
+  }
+  const response = await fetch(
+    "https://api.openai.com/v1/completions",
+    {
+      method: "POST",
+      headers: {
+        "Authorization": `Bearer ${apiKey}`,
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({
+        prompt,
+        max_tokens: 150,
+        temperature: 0,
+        // COMPLETION_MODEL of `code-davinci-002` may be better if you have access to it.
+        model: process.env.COMPLETION_MODEL || "text-davinci-002",
+        stop: ["\n\n"],
+      }),
+    },
+  );
+  if (response.status !== 200) {
+    log.error(`OpenAI API returned ${response.status}: ${await response.text()}`);
+    throw new Error(`OpenAI API returned status ${response.status}`);
+  }
+  const result = await response.json();
+  const completion = result.choices[0].text;
+  return completion;
+}
+
+async function sendForCompletionHuggingFace(prompt: string) {
+  const apiKey = process.env.HUGGINGFACE_API_KEY;
+  if (!apiKey) {
+    throw new Error("HUGGINGFACE_API_KEY not set");
+  }
+  // COMPLETION_MODEL values I've tried:
+  //   - codeparrot/codeparrot
+  //   - NinedayWang/PolyCoder-2.7B
+  //   - NovelAI/genji-python-6B
+  let completionUrl = process.env.COMPLETION_URL;
+  if (!completionUrl) {
+    if (process.env.COMPLETION_MODEL) {
+      completionUrl = `https://api-inference.huggingface.co/models/${process.env.COMPLETION_MODEL}`;
+    } else {
+      completionUrl = 'https://api-inference.huggingface.co/models/NovelAI/genji-python-6B';
+    }
+  }
+  let retries: number = 0;
+  let response!: FetchResponse;
+  while (retries++ < 3) {
+    response = await fetch(
+      completionUrl,
+      {
+        method: "POST",
+        headers: {
+          "Authorization": `Bearer ${apiKey}`,
+          "Content-Type": "application/json",
+        },
+        body: JSON.stringify({
+          inputs: prompt,
+          parameters: {
+            return_full_text: false,
+            max_new_tokens: 50,
+          },
+        }),
+      },
+    );
+    if (response.status === 503) {
+      log.error(`Sleeping for 10s - HuggingFace API returned ${response.status}: ${await response.text()}`);
+      await delay(10000);
+      continue;
+    }
+  }
+  if (response.status !== 200) {
+    const text = await response.text();
+    log.error(`HuggingFace API returned ${response.status}: ${text}`);
+    throw new Error(`HuggingFace API returned status ${response.status}: ${text}`);
+  }
+  const result = await response.json();
+  const completion = result[0].generated_text;
+  return completion.split('\n\n')[0];
+}
diff --git a/app/server/lib/DocWorker.ts b/app/server/lib/DocWorker.ts
index 180fa2e5..3a870dd5 100644
--- a/app/server/lib/DocWorker.ts
+++ b/app/server/lib/DocWorker.ts
@@ -110,6 +110,7 @@ export class DocWorker {
       applyUserActionsById:     activeDocMethod.bind(null, 'editors', 'applyUserActionsById'),
       findColFromValues:        activeDocMethod.bind(null, 'viewers', 'findColFromValues'),
       getFormulaError:          activeDocMethod.bind(null, 'viewers', 'getFormulaError'),
+      getAssistance:            activeDocMethod.bind(null, 'editors', 'getAssistance'),
       importFiles:              activeDocMethod.bind(null, 'editors', 'importFiles'),
       finishImportFiles:        activeDocMethod.bind(null, 'editors', 'finishImportFiles'),
       cancelImportFiles:        activeDocMethod.bind(null, 'editors', 'cancelImportFiles'),
diff --git a/documentation/llm.md b/documentation/llm.md
new file mode 100644
index 00000000..3a6b880d
--- /dev/null
+++ b/documentation/llm.md
@@ -0,0 +1,35 @@
+# Using Large Language Models with Grist
+
+In this experimental Grist feature, originally developed by Alex Hall,
+you can hook up an AI model such as OpenAI's Codex to write formulas for
+you. Here's how.
+
+First, you need an API key. You'll have best results currently with an
+OpenAI model. Visit https://openai.com/api/ and prepare a key, then
+store it in an environment variable `OPENAI_API_KEY`.
+
+Alternatively, there are many non-proprietary models hosted on Hugging Face.
+At the time of writing, none can compare with OpenAI for use with Grist.
+Things can change quickly in the world of AI though. So instead of OpenAI,
+you can visit https://huggingface.co/ and prepare a key, then
+store it in an environment variable `HUGGINGFACE_API_KEY`.
+
+That's all the configuration needed!
+
+Currently it is only a backend feature, we are still working on the UI for it.
+
+## Trying other models
+
+The model used will default to `text-davinci-002` for OpenAI. You can
+get better results by setting an environment variable `COMPLETION_MODEL` to
+`code-davinci-002` if you have access to that model.
+
+The model used will default to `NovelAI/genji-python-6B` for
+Hugging Face. There's no particularly great model for this application,
+but you can try other models by setting an environment variable
+`COMPLETION_MODEL` to `codeparrot/codeparrot` or
+`NinedayWang/PolyCoder-2.7B` or similar.
+
+If you are hosting a model yourself, host it as Hugging Face does,
+and use `COMPLETION_URL` rather than `COMPLETION_MODEL` to
+point to the model on your own server rather than Hugging Face.
diff --git a/sandbox/grist/formula_prompt.py b/sandbox/grist/formula_prompt.py
new file mode 100644
index 00000000..045013db
--- /dev/null
+++ b/sandbox/grist/formula_prompt.py
@@ -0,0 +1,186 @@
+import json
+import textwrap
+
+import six
+
+from column import is_visible_column, BaseReferenceColumn
+from objtypes import RaisedException
+import records
+
+
+def column_type(engine, table_id, col_id):
+  col_rec = engine.docmodel.get_column_rec(table_id, col_id)
+  typ = col_rec.type
+  parts = typ.split(":")
+  if parts[0] == "Ref":
+    return parts[1]
+  elif parts[0] == "RefList":
+    return "List[{}]".format(parts[1])
+  elif typ == "Choice":
+    return choices(col_rec)
+  elif typ == "ChoiceList":
+    return "Tuple[{}, ...]".format(choices(col_rec))
+  elif typ == "Any":
+    table = engine.tables[table_id]
+    col = table.get_column(col_id)
+    values = [col.raw_get(row_id) for row_id in table.row_ids]
+    return values_type(values)
+  else:
+    return dict(
+      Text="str",
+      Numeric="float",
+      Int="int",
+      Bool="bool",
+      Date="datetime.date",
+      DateTime="datetime.datetime",
+      Any="Any",
+      Attachments="Any",
+    )[parts[0]]
+
+
+def choices(col_rec):
+  try:
+    widget_options = json.loads(col_rec.widgetOptions)
+    return "Literal{}".format(widget_options["choices"])
+  except (ValueError, KeyError):
+    return 'str'
+
+
+def values_type(values):
+  types = set(type(v) for v in values) - {RaisedException}
+  optional = type(None) in types # pylint: disable=unidiomatic-typecheck
+  types.discard(type(None))
+
+  if types == {int, float}:
+    types = {float}
+
+  if len(types) != 1:
+    return "Any"
+
+  [typ] = types
+  val = next(v for v in values if isinstance(v, typ))
+
+  if isinstance(val, records.Record):
+    type_name = val._table.table_id
+  elif isinstance(val, records.RecordSet):
+    type_name = "List[{}]".format(val._table.table_id)
+  elif isinstance(val, list):
+    type_name = "List[{}]".format(values_type(val))
+  elif isinstance(val, set):
+    type_name = "Set[{}]".format(values_type(val))
+  elif isinstance(val, tuple):
+    type_name = "Tuple[{}, ...]".format(values_type(val))
+  elif isinstance(val, dict):
+    type_name = "Dict[{}, {}]".format(values_type(val.keys()), values_type(val.values()))
+  else:
+    type_name = typ.__name__
+
+  if optional:
+    type_name = "Optional[{}]".format(type_name)
+
+  return type_name
+
+
+def referenced_tables(engine, table_id):
+  result = set()
+  queue = [table_id]
+  while queue:
+    cur_table_id = queue.pop()
+    if cur_table_id in result:
+      continue
+    result.add(cur_table_id)
+    for col_id, col in visible_columns(engine, cur_table_id):
+      if isinstance(col, BaseReferenceColumn):
+        target_table_id = col._target_table.table_id
+        if not target_table_id.startswith("_"):
+          queue.append(target_table_id)
+  return result - {table_id}
+
+def all_other_tables(engine, table_id):
+  result = set(t for t in engine.tables.keys() if not t.startswith('_grist'))
+  return result - {table_id} - {'GristDocTour'}
+
+def visible_columns(engine, table_id):
+  return [
+    (col_id, col)
+    for col_id, col in engine.tables[table_id].all_columns.items()
+    if is_visible_column(col_id)
+  ]
+
+
+def class_schema(engine, table_id, exclude_col_id=None, lookups=False):
+  result = "@dataclass\nclass {}:\n".format(table_id)
+
+  if lookups:
+
+    # Build a lookupRecords and lookupOne method for each table, providing some arguments hints
+    # for the columns that are visible.
+    lookupRecords_args = []
+    lookupOne_args = []
+    for col_id, col in visible_columns(engine, table_id):
+      if col_id != exclude_col_id:
+        lookupOne_args.append(col_id + '=None')
+        lookupRecords_args.append('%s=%s' % (col_id, col_id))
+    lookupOne_args.append('sort_by=None')
+    lookupRecords_args.append('sort_by=sort_by')
+    lookupOne_args_line = ', '.join(lookupOne_args)
+    lookupRecords_args_line = ', '.join(lookupRecords_args)
+
+    result += "     def __len__(self):\n"
+    result += "        return len(%s.lookupRecords())\n" % table_id
+    result += "    @staticmethod\n"
+    result += "    def lookupRecords(%s) -> List[%s]:\n" % (lookupOne_args_line, table_id)
+    result += "       # ...\n"
+    result += "    @staticmethod\n"
+    result += "    def lookupOne(%s) -> %s:\n" % (lookupOne_args_line, table_id)
+    result += "       '''\n"
+    result += "       Filter for one result matching the keys provided.\n"
+    result += "       To control order, use e.g. `sort_by='Key' or `sort_by='-Key'`.\n"
+    result += "       '''\n"
+    result += "       return %s.lookupRecords(%s)[0]\n" % (table_id, lookupRecords_args_line)
+    result += "\n"
+
+  for col_id, col in visible_columns(engine, table_id):
+    if col_id != exclude_col_id:
+      result += "    {}: {}\n".format(col_id, column_type(engine, table_id, col_id))
+  result += "\n"
+  return result
+
+
+def get_formula_prompt(engine, table_id, col_id, description,
+                       include_all_tables=True,
+                       lookups=True):
+  result = ""
+  other_tables = (all_other_tables(engine, table_id)
+    if include_all_tables else referenced_tables(engine, table_id))
+  for other_table_id in sorted(other_tables):
+    result += class_schema(engine, other_table_id, lookups)
+
+  result += class_schema(engine, table_id, col_id, lookups)
+
+  return_type = column_type(engine, table_id, col_id)
+  result += "    @property\n"
+  result += "    # rec is alias for self\n"
+  result += "    def {}(rec) -> {}:\n".format(col_id, return_type)
+  result += '        """\n'
+  result += '{}\n'.format(indent(description, "        "))
+  result += '        """\n'
+  return result
+
+def indent(text, prefix, predicate=None):
+  """
+  Copied from https://github.com/python/cpython/blob/main/Lib/textwrap.py for python2 compatibility.
+  """
+  if six.PY3:
+    return textwrap.indent(text, prefix, predicate) # pylint: disable = no-member
+  if predicate is None:
+    def predicate(line):
+      return line.strip()
+  def prefixed_lines():
+    for line in text.splitlines(True):
+      yield (prefix + line if predicate(line) else line)
+  return ''.join(prefixed_lines())
+
+def convert_completion(completion):
+  result = textwrap.dedent(completion)
+  return result
diff --git a/sandbox/grist/main.py b/sandbox/grist/main.py
index 89b2f499..952efd66 100644
--- a/sandbox/grist/main.py
+++ b/sandbox/grist/main.py
@@ -16,6 +16,7 @@ import six
 
 import actions
 import engine
+import formula_prompt
 import migrations
 import schema
 import useractions
@@ -135,6 +136,14 @@ def run(sandbox):
   def get_formula_error(table_id, col_id, row_id):
     return objtypes.encode_object(eng.get_formula_error(table_id, col_id, row_id))
 
+  @export
+  def get_formula_prompt(table_id, col_id, description):
+    return formula_prompt.get_formula_prompt(eng, table_id, col_id, description)
+
+  @export
+  def convert_formula_completion(completion):
+    return formula_prompt.convert_completion(completion)
+
   export(parse_acl_formula)
   export(eng.load_empty)
   export(eng.load_done)
diff --git a/sandbox/grist/test_formula_prompt.py b/sandbox/grist/test_formula_prompt.py
new file mode 100644
index 00000000..6cceb1db
--- /dev/null
+++ b/sandbox/grist/test_formula_prompt.py
@@ -0,0 +1,217 @@
+import unittest
+import six
+
+import test_engine
+import testutil
+
+from formula_prompt import (
+  values_type, column_type, referenced_tables, get_formula_prompt,
+)
+from objtypes import RaisedException
+from records import Record, RecordSet
+
+
+class FakeTable(object):
+
+  def __init__(self):
+    pass
+
+  table_id = "Table1"
+  _identity_relation = None
+
+
+@unittest.skipUnless(six.PY3, "Python 3 only")
+class TestFormulaPrompt(test_engine.EngineTestCase):
+  def test_values_type(self):
+    self.assertEqual(values_type([1, 2, 3]), "int")
+    self.assertEqual(values_type([1.0, 2.0, 3.0]), "float")
+    self.assertEqual(values_type([1, 2, 3.0]), "float")
+
+    self.assertEqual(values_type([1, 2, None]), "Optional[int]")
+    self.assertEqual(values_type([1, 2, 3.0, None]), "Optional[float]")
+
+    self.assertEqual(values_type([1, RaisedException(None), 3]), "int")
+    self.assertEqual(values_type([1, RaisedException(None), None]), "Optional[int]")
+
+    self.assertEqual(values_type(["1", "2", "3"]), "str")
+    self.assertEqual(values_type([1, 2, "3"]), "Any")
+    self.assertEqual(values_type([1, 2, "3", None]), "Any")
+
+    self.assertEqual(values_type([
+      Record(FakeTable(), None),
+      Record(FakeTable(), None),
+    ]), "Table1")
+    self.assertEqual(values_type([
+      Record(FakeTable(), None),
+      Record(FakeTable(), None),
+      None,
+    ]), "Optional[Table1]")
+
+    self.assertEqual(values_type([
+      RecordSet(FakeTable(), None),
+      RecordSet(FakeTable(), None),
+    ]), "List[Table1]")
+    self.assertEqual(values_type([
+      RecordSet(FakeTable(), None),
+      RecordSet(FakeTable(), None),
+      None,
+    ]), "Optional[List[Table1]]")
+
+    self.assertEqual(values_type([[1, 2, 3]]), "List[int]")
+    self.assertEqual(values_type([[1, 2, 3], None]), "Optional[List[int]]")
+    self.assertEqual(values_type([[1, 2, None]]), "List[Optional[int]]")
+    self.assertEqual(values_type([[1, 2, None], None]), "Optional[List[Optional[int]]]")
+    self.assertEqual(values_type([[1, 2, "3"]]), "List[Any]")
+
+    self.assertEqual(values_type([{1, 2, 3}]), "Set[int]")
+    self.assertEqual(values_type([(1, 2, 3)]), "Tuple[int, ...]")
+    self.assertEqual(values_type([{1: ["2"]}]), "Dict[int, List[str]]")
+
+  def assert_column_type(self, col_id, expected_type):
+    self.assertEqual(column_type(self.engine, "Table2", col_id), expected_type)
+
+  def assert_prompt(self, table_name, col_id, expected_prompt):
+    prompt = get_formula_prompt(self.engine, table_name, col_id, "description here",
+                                include_all_tables=False, lookups=False)
+    # print(prompt)
+    self.assertEqual(prompt, expected_prompt)
+
+  def test_column_type(self):
+    sample = testutil.parse_test_sample({
+      "SCHEMA": [
+        [1, "Table2", [
+          [1, "text", "Text", False, "", "", ""],
+          [2, "numeric", "Numeric", False, "", "", ""],
+          [3, "int", "Int", False, "", "", ""],
+          [4, "bool", "Bool", False, "", "", ""],
+          [5, "date", "Date", False, "", "", ""],
+          [6, "datetime", "DateTime", False, "", "", ""],
+          [7, "attachments", "Attachments", False, "", "", ""],
+          [8, "ref", "Ref:Table2", False, "", "", ""],
+          [9, "reflist", "RefList:Table2", False, "", "", ""],
+          [10, "choice", "Choice", False, "", "", '{"choices": ["a", "b", "c"]}'],
+          [11, "choicelist", "ChoiceList", False, "", "", '{"choices": ["x", "y", "z"]}'],
+          [12, "ref_formula", "Any", True, "$ref or None", "", ""],
+          [13, "numeric_formula", "Any", True, "1 / $numeric", "", ""],
+          [14, "new_formula", "Numeric", True, "'to be generated...'", "", ""],
+        ]],
+      ],
+      "DATA": {
+        "Table2": [
+          ["id", "numeric", "ref"],
+          [1, 0, 0],
+          [2, 1, 1],
+        ],
+      },
+    })
+    self.load_sample(sample)
+
+    self.assert_column_type("text", "str")
+    self.assert_column_type("numeric", "float")
+    self.assert_column_type("int", "int")
+    self.assert_column_type("bool", "bool")
+    self.assert_column_type("date", "datetime.date")
+    self.assert_column_type("datetime", "datetime.datetime")
+    self.assert_column_type("attachments", "Any")
+    self.assert_column_type("ref", "Table2")
+    self.assert_column_type("reflist", "List[Table2]")
+    self.assert_column_type("choice", "Literal['a', 'b', 'c']")
+    self.assert_column_type("choicelist", "Tuple[Literal['x', 'y', 'z'], ...]")
+    self.assert_column_type("ref_formula", "Optional[Table2]")
+    self.assert_column_type("numeric_formula", "float")
+
+    self.assertEqual(referenced_tables(self.engine, "Table2"), set())
+
+    self.assert_prompt("Table2", "new_formula",
+      '''\
+@dataclass
+class Table2:
+    text: str
+    numeric: float
+    int: int
+    bool: bool
+    date: datetime.date
+    datetime: datetime.datetime
+    attachments: Any
+    ref: Table2
+    reflist: List[Table2]
+    choice: Literal['a', 'b', 'c']
+    choicelist: Tuple[Literal['x', 'y', 'z'], ...]
+    ref_formula: Optional[Table2]
+    numeric_formula: float
+
+    @property
+    # rec is alias for self
+    def new_formula(rec) -> float:
+        """
+        description here
+        """
+''')
+
+  def test_get_formula_prompt(self):
+    sample = testutil.parse_test_sample({
+      "SCHEMA": [
+        [1, "Table1", [
+          [1, "text", "Text", False, "", "", ""],
+        ]],
+        [2, "Table2", [
+          [2, "ref", "Ref:Table1", False, "", "", ""],
+        ]],
+        [3, "Table3", [
+          [3, "reflist", "RefList:Table2", False, "", "", ""],
+        ]],
+      ],
+      "DATA": {},
+    })
+    self.load_sample(sample)
+    self.assertEqual(referenced_tables(self.engine, "Table3"), {"Table1", "Table2"})
+    self.assertEqual(referenced_tables(self.engine, "Table2"), {"Table1"})
+    self.assertEqual(referenced_tables(self.engine, "Table1"), set())
+
+    self.assert_prompt("Table1", "text", '''\
+@dataclass
+class Table1:
+
+    @property
+    # rec is alias for self
+    def text(rec) -> str:
+        """
+        description here
+        """
+''')
+
+    self.assert_prompt("Table2", "ref", '''\
+@dataclass
+class Table1:
+    text: str
+
+@dataclass
+class Table2:
+
+    @property
+    # rec is alias for self
+    def ref(rec) -> Table1:
+        """
+        description here
+        """
+''')
+
+    self.assert_prompt("Table3", "reflist", '''\
+@dataclass
+class Table1:
+    text: str
+
+@dataclass
+class Table2:
+    ref: Table1
+
+@dataclass
+class Table3:
+
+    @property
+    # rec is alias for self
+    def reflist(rec) -> List[Table2]:
+        """
+        description here
+        """
+''')