Software

An instance of LLM prompting for programming

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Xu Hao makes use of chain of thought and basic information 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 slightly than code (basic
information prompting). As soon as he has the plan he makes use of it to refine the
implementation and generate helpful sections of code.

Just lately I watched an interesting 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 means 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 needs the code to be structured.

The present system is an internet 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
checks for view.

All codes must be written within the tech stack talked about above.
Necessities must be carried out as react parts 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 carried out as Redux retailer slice. Examined in
vitest.

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

3. Hooks are used as the foremost view helpers to retrieve knowledge from shared
view mannequin. For many the case, it's going to 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 an alternative. Within the interface,
every redux motion is mapped to a way. Examined in vitest.

5. View is encompass konva shapes, and carried out as react element through
react-konva. Examined in cypress element checks 

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

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

2. Information-driven checks are most popular.

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
info) on the whiteboard. 

AC1: Don’t show native person

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

Present an total resolution following the steering talked about above.
Trace, hold all consciousness info 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 quite a bit happening with this immediate, so he highlighted a couple of
factors.

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

Many of the immediate is setting out the design tips that he needs
ChatGPT’s generated code to observe. He refined this by placing prompts into
ChatGPT, trying on the end result, and crafting the immediate to make it generate
within the model 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
group.

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

Although the ultimate aim of this session is to generate code and its
checks, at this stage he particularly mentioned Don’t generate code.
At this level he needs ChatGPT to stipulate its method earlier than we glance to any
code technology. This can be a immediate approach known as Generated Information.

A typical means of interacting with an LLM is to offer it an issue and ask
it to generate some last product, whether or not than be prose or (on this case)
code. With Generated Information we break up our interplay into separate steps:
first ask the LLM to generate some helpful details about the issue, and
then we feed that info again into the LLM to generate the ultimate
product.

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

  • Instruction prompting gives directions for the LLM to observe
  • Chain of Thought prompting encourages the LLM to elucidate its
    reasoning.

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

Answer:

To implement the notice layer function, we are able to observe the rules talked about in the issue assertion and use the present tech stack.

Process Checklist:

  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” parts.

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

  4. Create a brand new Redux slice to retailer the notice info for distant customers.

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

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

  7. Implement a way within the encapsulated view mannequin interface to replace the notice info for every distant person.

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

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

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

  11. Write checks for the encapsulated view mannequin interface to make sure it correctly updates the notice info for distant customers.

  12. Write end-to-end Cypress checks to make sure the notice 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 notice layer function within the on-line whiteboard system.

The response from ChatGPT is non-deterministic, so this result’s simply
one run. In the event you (or I) put this immediate in once more, we’ll get a distinct response.

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

The LLM responds with a plan.

Xu Hao can now have a look at the grasp plan and see if it is smart. If
there are issues with it, he can ask ChatGPT to re-write a few of the duties
or your entire grasp plan by offering extra info.

For instance, the present grasp plan was not grouped by differing types
of parts. One attainable immediate may be:

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

This could possibly be helpful to confirm the alignment between the structure
imaginative and prescient and grasp plan.

These tweaks could possibly be added to the preliminary immediate to get an total
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 an alternative. Within the interface,
    every redux motion is mapped to a way. Examined in vitest.

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

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:

Process Checklist:

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

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

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

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

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

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

  7. Implement the “updateUserAwarenessInfo” methodology within the “AwarenessViewModel” interface to dispatch a Redux motion to replace the notice info for every distant person. This methodology might be known as by the “MainWhiteboard” element.

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

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

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

  11. Write checks for the encapsulated view mannequin interface to make sure it correctly updates the notice info for distant customers. These checks will use the Vitest framework.

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

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

present me instance checks and implementation for the notice slice
and AwarenessViewModel. Check first.

Certain, let's begin with writing checks for the notice 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<{ x: quantity; y: quantity }>) => {
      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 {
  non-public 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 unhealthy 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 means of a
sequence of rewrites. Here is one to get rig of redux-mock-store.

rewrite the checks with out redux-mock-store

Certain, here is an instance implementation of the checks for the AwarenessViewModel and the notice 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: Document<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 proper motion was dispatched utilizing jest.fn().

Additional rewrites can match in additional changes, akin to asking it to
Use `configureTestingStore(‘consciousness’, initialState)` to initialize
the take a look at retailer
can get it to slot in with the group’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 fantastic.

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 information prompting approaches is usually a considerably useful gizmo
for programming. Particularly it reveals that to make use of LLMs properly, we have to
discover ways to assemble prompts to get the perfect outcomes. This expertise
means that it is helpful to work together with the LLM like a junior associate,
beginning them with architectural tips, asking them to point out their
reasoning, and tweaking their outputs as we go.


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