An instance of LLM prompting for programming

Xu Hao makes use of chain of thought and common data prompting with ChatGPT when writing self-testing code

My account of an inside chat with Xu Hao, the place he reveals how he
drives ChatGPT to provide helpful self-tested code. His preliminary immediate primes
the LLM with an implementation technique (chain of thought prompting). His
immediate additionally asks for an implementation plan fairly than code (common
data prompting). As soon as he has the plan he makes use of it to refine the
implementation and generate helpful sections of code.

Not too long ago I watched an enchanting name on Zoom. Xu Hao, Thoughtworks’s
Head of Expertise in China, defined his explorations in utilizing ChatGPT to
assist construct Self Testing Code. He
went by way of the sort of interplay that works for him.

He begins with a immediate that units the context for the appliance and the way
he desires the code to be structured.

The present system is a web based whiteboard system. Tech stack:
typescript, react, redux, konvajs and react-konva. And vitest, react
testing library for mannequin, view mannequin and associated hooks, cypress element
exams for view.

All codes ought to be written within the tech stack talked about above.
Necessities ought to be applied as react elements within the MVVM
structure sample.

There are 2 sorts
of view mannequin within the system.

1. Shared view mannequin. View mannequin that represents states shared amongst
native and distant customers.

2. Native view mannequin. View mannequin that represents states solely relevant
to native person

Listed here are the frequent implementation technique:

1. Shared view mannequin is applied as Redux retailer slice. Examined in
vitest.

2. Native view mannequin is applied as React element props or states(by
useState hook), until for world native view mannequin, which can also be
applied as Redux retailer slice. Examined in vitest.

3. Hooks are used as the foremost view helpers to retrieve information from shared
view mannequin. For many the case, it can use ‘createSelector’ and
‘useSelector’ for memorization. Examined in vitest and react testing
library.

4. Don’t dispatch motion immediately to vary the states of shared view
mannequin, use an encapsulated view mannequin interface as a substitute. Within the interface,
every redux motion is mapped to a technique. Examined in vitest.

5. View is include konva shapes, and applied as react element through
react-konva. Examined in cypress element exams 

Listed here are sure patterns ought to be adopted when implement and take a look at
the element

1. When write take a look at, use `describe` as a substitute of `take a look at`

2. Information-driven exams are most well-liked.

3. When take a look at the view element, faux view mannequin through the view mannequin
interface

Consciousness Layer 

Requirement: 

Show different customers’ consciousness information(cursor, title and on-line
data) on the whiteboard. 

AC1: Don’t show native person

AC2: When distant person modifications cursor location, show the change in
animation. 

Present an general resolution following the steering talked about above.
Trace, maintain all consciousness data in a Konva layer, and an consciousness
information element to render cursor, and title. Don’t generate code. Describe
the answer, and breaking the answer down as a process record primarily based on the
steering talked about above. And we'll refer this process record as our grasp
plan.

There’s rather a lot happening with this immediate, so he highlighted just a few
factors.

He is utilizing a generic utility instance in right here: one factor to be
cautious of when interacting with ChatGPT and the like is that we should always by no means
put something which may be confidential into the immediate
, as that might be a
safety threat. Enterprise guidelines, any code from an actual venture – all these should
not enter the interplay with ChatGPT.

Many of the immediate is setting out the design pointers that he desires
ChatGPT’s generated code to comply with. He refined this by placing prompts into
ChatGPT, trying on the consequence, and crafting the immediate to make it generate
within the type he wished. As soon as he has context working, he can paste it into
each session he has with ChatGPT, and share it with the remainder of the
workforce.

Xu Hao begins his session by prompting the
ChatGPT LLM with context in addition to the instruction to generate a plan

Regardless that the ultimate aim of this session is to generate code and its
exams, at this stage he particularly mentioned Don’t generate code.
At this level he desires ChatGPT to stipulate its method earlier than we glance to any
code era. It is a immediate method referred to as Generated Data.

A standard method of interacting with an LLM is to present it an issue and ask
it to generate some closing product, whether or not than be prose or (on this case)
code. With Generated Data we cut up our interplay into separate steps:
first ask the LLM to generate some helpful details about the issue, and
then we feed that data again into the LLM to generate the ultimate
product.

Probably the most attention-grabbing a part of the immediate is the “implementation technique”
part which mixes two kinds of immediate:

  • Instruction prompting supplies directions for the LLM to comply with
  • Chain of Thought prompting encourages the LLM to clarify its
    reasoning.

Within the Implementation Technique part, Xu Hao described the specified
structure sample as an anticipated “Chain of Thought directions” for
ChatGPT to comply with. Then he instructed ChatGPT to construct a process record (the
generated data) primarily based on this chain of thought.

Answer:

To implement the attention layer characteristic, we are able to comply with the rules talked about in the issue assertion and use the present tech stack.

Job Listing:

  1. Create a brand new element named “AwarenessInfo” to render cursor and title for distant customers.

  2. Create a brand new Konva layer to carry all of the “AwarenessInfo” elements.

  3. Add the Konva layer to the principle whiteboard element.

  4. Create a brand new Redux slice to retailer the attention data for distant customers.

  5. Create an encapsulated view mannequin interface for the attention Redux slice.

  6. Implement the “AwarenessInfo” element to retrieve and show the attention data for every distant person from the view mannequin.

  7. Implement a technique within the encapsulated view mannequin interface to replace the attention data for every distant person.

  8. When a distant person modifications the cursor location, dispatch the replace technique within the encapsulated view mannequin interface to replace the corresponding consciousness data.

  9. Within the “AwarenessInfo” element, use the brand new location to animate the cursor change.

  10. Write exams for the brand new “AwarenessInfo” element, together with data-driven exams to make sure it correctly shows the attention data for all distant customers.

  11. Write exams for the encapsulated view mannequin interface to make sure it correctly updates the attention data for distant customers.

  12. Write end-to-end Cypress exams to make sure the attention layer is correctly displayed and up to date when distant customers change their cursor location.

By following these duties, we are able to implement the attention layer characteristic within the on-line whiteboard system.

The response from ChatGPT is non-deterministic, so this result’s simply
one run. For those who (or I) put this immediate in once more, we’ll get a special response.

The important thing ingredient of the response is an ordered process record, with numbers for
every step. We will use these numbers to refer to those duties for the remainder of
the session.

The LLM responds with a plan.

Xu Hao can now take a look at the grasp plan and see if it is sensible. If
there are issues with it, he can ask ChatGPT to re-write a number of the duties
or the complete grasp plan by offering extra data.

For instance, the present grasp plan was not grouped by differing types
of elements. One doable immediate could be:

Rewrite the grasp plan, and group duties as mannequin, view mannequin and consider.

This may very well be helpful to confirm the alignment between the structure
imaginative and prescient and grasp plan.

These tweaks may very well be added to the preliminary immediate to get an general
higher response.For this explicit immediate, the next instruction was
not within the preliminary immediate, however had been added in later iteration:

4. Don’t dispatch motion immediately to vary the states of shared view
    mannequin, use an encapsulated view mannequin interface as a substitute. Within the interface,
    every redux motion is mapped to a technique. Examined in vitest.

As soon as the grasp plan appears affordable, Xu Hao asks to regenerate it with
names for this system parts.

Rewrite the grasp plan, this time with detailed element names,
strategies, and props

Certain, here is a revised grasp plan with detailed element names, strategies, and props:

Job Listing:

  1. Create a brand new element named “AwarenessInfo” to render cursor and title for distant customers. This element will obtain props equivalent to person title, cursor place, and on-line standing.

  2. Create a brand new Konva layer named “AwarenessLayer” to carry all of the “AwarenessInfo” elements.

  3. Add the “AwarenessLayer” to the principle whiteboard element.

  4. Create a brand new Redux slice named “consciousness” to retailer the attention data for distant customers.

  5. Create an encapsulated view mannequin interface named “AwarenessViewModel” for the “consciousness” Redux slice. This interface will comprise a technique named “updateUserAwarenessInfo” to replace the attention data for every distant person.

  6. Implement the “AwarenessInfo” element to retrieve and show the attention data for every distant person from the view mannequin. The element will use the “useSelector” hook to retrieve the related data.

  7. Implement the “updateUserAwarenessInfo” technique within the “AwarenessViewModel” interface to dispatch a Redux motion to replace the attention data for every distant person. This technique will likely be referred to as by the “MainWhiteboard” element.

  8. When a distant person modifications the cursor location, dispatch the “updateUserAwarenessInfo” technique within the “AwarenessViewModel” interface to replace the corresponding consciousness data.

  9. Within the “AwarenessInfo” element, use the brand new location to animate the cursor change. This may be achieved utilizing the Konva “Tween” animation.

  10. Write exams for the brand new “AwarenessInfo” element, together with data-driven exams to make sure it correctly shows the attention data for all distant customers. The exams will likely be written utilizing the React Testing Library.

  11. Write exams for the encapsulated view mannequin interface to make sure it correctly updates the attention data for distant customers. These exams will use the Vitest framework.

  12. Write end-to-end Cypress exams to make sure the attention layer is correctly displayed and up to date when distant customers change their cursor location. The exams will simulate person interactions with the whiteboard and confirm the state of the “AwarenessLayer” and “AwarenessInfo” elements.

At this level, Xu Hao is able to ask ChatGPT to supply code for one in all
the steps

present me instance exams and implementation for the attention slice
and AwarenessViewModel. Check first.

Certain, let's begin with writing exams for the attention slice and AwarenessViewModel:

Check for Consciousness Slice:

describe('consciousness slice', () => 
  let retailer: MockStore;
  beforeEach(() => 
    retailer = configureMockStore()(initialState);
  );

  it('ought to set the cursor location', () => 
    retailer.dispatch(setCursorLocation( x: 100, y: 200 ));
    const state = retailer.getState().consciousness;
    count on(state.cursorLocation).toEqual( x: 100, y: 200 );
  );

  it('ought to set the title', () => 
    retailer.dispatch(setName('John Doe'));
    const state = retailer.getState().consciousness;
    count on(state.title).toBe('John Doe');
  );

  it('ought to set the net standing', () => 
    retailer.dispatch(setOnline(true));
    const state = retailer.getState().consciousness;
    count on(state.isOnline).toBe(true);
  );
);
Check for AwarenessViewModel:

describe('AwarenessViewModel', () => 
  let retailer: MockStore;
  let awarenessViewModel: AwarenessViewModel;
  beforeEach(() => 
    retailer = configureMockStore()(initialState);
    awarenessViewModel = new AwarenessViewModel(retailer);
  );

  it('ought to set the cursor location', () => 
    awarenessViewModel.setCursorLocation( x: 100, y: 200 );
    const state = retailer.getState().consciousness;
    count on(state.cursorLocation).toEqual( x: 100, y: 200 );
  );

  it('ought to set the title', () => 
    awarenessViewModel.setName('John Doe');
    const state = retailer.getState().consciousness;
    count on(state.title).toBe('John Doe');
  );

  it('ought to set the net standing', () => 
    awarenessViewModel.setOnline(true);
    const state = retailer.getState().consciousness;
    count on(state.isOnline).toBe(true);
  );
);
Now, let's implement the Consciousness slice and AwarenessViewModel:

Consciousness slice:

interface AwarenessState 
  cursorLocation:  x: quantity; y: quantity ;
  title: string;
  isOnline: boolean;


const initialState: AwarenessState = 
  cursorLocation:  x: 0, y: 0 ,
  title: '',
  isOnline: false,
;

const awarenessSlice = createSlice(
  title: 'consciousness',
  initialState,
  reducers: 
    setCursorLocation: (state, motion: PayloadAction) => 
      state.cursorLocation = motion.payload;
    ,
    setName: (state, motion: PayloadAction) => 
      state.title = motion.payload;
    ,
    setOnline: (state, motion: PayloadAction) => 
      state.isOnline = motion.payload;
    ,
  ,
);

export const  setCursorLocation, setName, setOnline  = awarenessSlice.actions;

export default awarenessSlice.reducer;
AwarenessViewModel:

class AwarenessViewModel 
  personal retailer: Retailer;

  constructor(retailer: Retailer) 
    this.retailer = retailer;
  

  setCursorLocation(cursorLocation:  x: quantity; y: quantity ) 
    this.retailer.dispatch(setCursorLocation(cursorLocation));
  

  setName(title: string) 
    this.retailer.dispatch(setName(title));
  

  setOnline(isOnline: boolean) 
    this.retailer.dispatch(setOnline(isOnline));
  


Word: On this implementation, I assumed that the AwarenessViewModel is used to
set the values within the retailer. If it is usually used to retrieve the values from the
retailer, we are able to add corresponding strategies to the AwarenessViewModel class

(I’ve eliminated the styling from ChatGPT’s response.)

General this code is neither dangerous nor good. It will not run because it stands.
It is lacking a configureMockStore and assumes it makes use of
redux-mock-store, which is not a part of the tech stack. Xu Hao can repair these
issues by asking the LLM to progressively tweak the code by way of a
sequence of rewrites. Here is one to get rig of redux-mock-store.

rewrite the exams with out redux-mock-store

Certain, here is an instance implementation of the exams for the AwarenessViewModel and the attention slice with out utilizing redux-mock-store:

import createAwarenessSlice, AwarenessState, updateAwareness from './awarenessSlice'; import AwarenessViewModel from './AwarenessViewModel'; describe('AwarenessViewModel', () => { const mockDispatch = jest.fn(); const initialState: AwarenessState = customers: 'user1': title: 'Person 1', cursorPosition: x: 0, y: 0 , on-line: true, , 'user2': title: 'Person 2', cursorPosition: x: 100, y: 100 , on-line: true, , , ; let viewModel: AwarenessViewModel; beforeEach(() => viewModel = new AwarenessViewModel(initialState, mockDispatch); ); describe('getUserNames', () => it('returns the record of person names', () => const names = viewModel.getUserNames(); count on(names).toEqual(['User 1', 'User 2']); ); ); describe('getCursorPosition', () => it('returns the cursor place for a given person ID', () => const cursorPosition = viewModel.getCursorPosition('user1'); count on(cursorPosition).toEqual( x: 0, y: 0 ); ); it('returns undefined for an unknown person ID', () => const cursorPosition = viewModel.getCursorPosition('unknown'); count on(cursorPosition).toBeUndefined(); ); ); describe('updateCursorPosition', () => it('dispatches the updateAwareness motion with the brand new cursor place', () => viewModel.updateCursorPosition('user1', x: 50, y: 50 ); count on(mockDispatch).toHaveBeenCalledWith(updateAwareness( person: 'user1', cursorPosition: x: 50, y: 50 , )); ); ); });

And here is an instance implementation of the awarenessSlice with out utilizing redux-mock-store:

import createSlice, PayloadAction from '@reduxjs/toolkit'; export sort CursorPosition = x: quantity, y: quantity ; export sort UserAwareness = title: string; cursorPosition: CursorPosition; on-line: boolean; ; export sort AwarenessState = customers: Report<string, UserAwareness>; ; const initialState: AwarenessState = customers: , ; const awarenessSlice = createSlice( title: 'consciousness', initialState, reducers: updateAwareness: (state, motion: PayloadAction< person: string, cursorPosition: CursorPosition >) => const person, cursorPosition = motion.payload; state.customers[user].cursorPosition = cursorPosition; , setOnline: (state, motion: PayloadAction< person: string, on-line: boolean >) => const person, on-line = motion.payload; state.customers[user].on-line = on-line; , , ); export const updateAwareness, setOnline = awarenessSlice.actions; export const awarenessReducer = awarenessSlice.reducer;

Word that since we’re not utilizing redux-mock-store, we have to move a mock dispatch perform to the view mannequin constructor, and within the take a look at for updateCursorPosition we have to verify that the right motion was dispatched utilizing jest.fn().

Additional rewrites can match in additional changes, equivalent to asking it to
Use `configureTestingStore(‘consciousness’, initialState)` to initialize
the take a look at retailer
can get it to slot in with the workforce’s take a look at idioms. Then
with some path changes and a small quantity of glue for the reducers, the
code can run simply effective.

As soon as that is working, Xu Hao can repeat the method for the remainder of the
duties within the grasp plan.

My take away from this dialogue was that utilizing chain of thought and
generated data prompting approaches is usually a considerably useful gizmo
for programming. Specifically it reveals that to make use of LLMs properly, we have to
learn to assemble prompts to get the perfect outcomes. This expertise
means that it is helpful to work together with the LLM like a junior companion,
beginning them with architectural pointers, asking them to indicate their
reasoning, and tweaking their outputs as we go.