शैडोइंग अभ्यास: The 7 phases of AI-driven development - वीडियो के साथ अंग्रेजी बोलना सीखें

अभ्यास मोड चुनें

हर मोड पूरी स्क्रीन का अभ्यास कक्ष खोलता है।

104 वाक्य

यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।

What's up friends, I'm going to keep this short and sweet.

I have identified seven phases of development with AI.

In other words, as you're working through coding with your AI coding assistant, in my case Claude Code usually, then these are the seven phases you should be thinking about for shipping great work.

The way you achieve these phases is kind of up to you.

There are many different implementations of it, but these are the ones that I have understood to be common across lots and lots of different approaches.

Whether you're doing Ralph loops like I mostly am, whether you're doing GSD, whether you're using spec kit, you are probably going to be using these seven phases.

If you dig this stuff and you believe that engineering fundamentals are really important in the AI age, then guess what?

So do I.

And this is what I cover and elaborate on in my newsletter.

This is not for vibe coders.

We are people that are serious about AI engineering and serious about building applications that are built to last.

So if that sounds like you and you want to improve your skills, then this is the place.

But without Without further ado, let's go into the list.

Phase 1 we start with the idea.

You have some kind of idea, some reason that you are invoking this progress, something that you want the AI to do for you.

This might be that you have an entire app idea that you want to build.

Or you might just have a narrow thing that you want to complete within the codebase that you're in, like a bug fix or a feature.

I also count refactors as part of this too, so if you have a codebase that you need to refactor then this process will work for you too.

This idea can be as small and as big as you like.

We can expand this idea and this process can take very, very large ideas and turn them into reality.

Or it can be teeny, very narrow and very focused.

Doesn't matter.

Now just to give you a glimpse of the future setup here, the idea is going to be turned into a set of tickets which a kind of AI is going to complete.

Now that set of tickets might end up being lots

and lots of different kind of like AIs working at once

or maybe just a big list of tasks that the AI is going to complete sequentially.

So if this idea involves any kind of research here, any kind of like difficult explore phases as part of building the code,

then you may want to include a research phase now.

For instance, if you're doing like a Stripe integration or maybe integrating with an API that's not very common, then you might want to create an asset

that kind of takes all of the research about

that thing like based on your idea and kind of caches it

and puts it inside the repo or somewhere that your agent can access.

Essentially, every time your agent is doing work, it might need to explore the repo in a fresh context window.

And if that exploration is difficult, so it's in an external API or it's somewhere that's hard to access,

then you'll want to cache it in a research.md asset and you'll definitely want to run a research phase at this point.

The next step after research is to get to prototyping.

Now in the prototype stage we're still not really sure what we're actually building on even maybe why we're building it.

Prototyping is really important if you need to impose your taste on the outcome.

In other words, maybe you need some UI that needs to look a certain way or behave a certain

you're not quite sure which one to do.

What I tend to do is just chuck up a bunch of different ideas on a throwaway route, which is kind of like the LLM showing me all of

the different ways it can think of to build out the prototype.

I then iterate on the prototype inside a couple of sessions and say, okay, no, that one looks like the best.

I found that doing this early is absolutely essential

because then you can actually commit the prototype to your codebase

and then make that available to the agent when it actually goes to implement it.

The next step, we are in step four now, is to create a PRD.

Now that we understand a bit more about the external APIs that we're using in the research phase, now that we understand a bit more about the prototype and we've actually seen some code,

it's time to start actually properly describing the destination.

We should now feel confident in ourselves that we can kind of understand the end state, what we're trying to create at the end.

We won't know all of the implementation decisions yet, we will just kind of know the basic stuff

that the user is going to see and the way that it's going to behave.

We don't have to call this a PRD by the way, this is a PRD is a product requirements document, but really it's just some kind of document that describes the end state of where we're going.

Now in the process of creating this end state, we really need to hammer out the design.

And this means we need to prompt the agent to absolutely grill us walking down every part of our decision tree.

I have a writer PRD skill that is purpose designed for this, which I will link to below if you're interested.

But once we've created the PRD, then it's time to actually start breaking down the PRD into some kind of implementation plan.

For those of you who are not developers, or you've never used a Kanban board or a Jira board or anything like that,

a Kanban board is just a list of tickets that have blocking relationships between them.

We're essentially just describing the work that needs to be done.

So I then have a separate skill for turning my PRD into separate issues.

We could create a single sequential plan that turns the PRD into like actual code, but with a Kanban board you actually get to parallelize really effectively.

And so I can just literally go on my Kanban board, all of the tickets that aren't blocking and spin up an agent for each one and get it to resolve it.

But of course what I'm starting to talk about here is execution.

So in some kind of loop here run a coding agent to execute all of the tickets on the Kanban board.

Most times you won't need to parallelise this, most times a sequential agent just working through each ticket will be enough.

And for me this is a Ralph loop which works really really effectively with this setup.

And I'll drop some links below on writing about Ralph that I've done.

once you've done with execution and you've got a completed asset for you to actually look at,

then you get the agent to create a QA plan for the human to QA the completed work.

And what this usually results in is more tasks in the Kanban board and going through the execution loop again.

So you will tend to loop these last three steps quite a few times until you iterate towards a perfect product.

And QA here also involves a human actually going and reading the code that has been produced during the execution loop.

That might not always be needed, especially if you're using a kind of gray box architecture that I've talked about in previous videos.

But overall, these seven phases are the things I'm thinking about whenever I'm working with an AI agent.

We start with the idea, some kind of app or feature or refactor.

If we know there are external dependencies and difficult to execute explore phases, then we cache it in a research phase.

And by the way, this research generally only lives for the lifetime of this sprint, essentially, or the lifetime of the idea that we're imposing on the app.

The reason for that is that research can go out of date, or it can just rot away, essentially, and actually cause our agent to take a wrong turn where it's not needed.

If I need to impose my taste, then I will use a prototype here.

So I will really just sit with an agent, human in the loop, to hash out some ideas.

This is not just for design as well it can be for software architecture too

or let's say testing something out with an external service.

This is an essential step because by the time we get to the PRD it's a little bit too abstract.

You really need concrete feedback first.

Then I write the PRD which is the documentation, the spec for where we are going.

Next I make a kind of understanding of the journey towards the PRD by turning it into a Kanban board.

I generally use GitHub issues for both the PRD and the Kanban board by the way, it's just an easy thing I've found.

Although GitHub doesn't have yet a kind of built in way to represent blocking relationships between tickets, so you might be just better off with something like linear which does.

Once the Kanban board is all ready and set up then I execute it in some kind of loop, for me that's a Ralph loop.

You could also I suppose do execution human in the loop style where you sit and execute the tickets individually.

But I generally find with all of this set up, with the research, with the prototype with the Kanban board with the PRD helping it, you can totally run this execution loop AFK and the results will be really good.

And to make sure

that they're really good we then enter a QA phase where we get the agent to produce a QA plan.

Then a human, yes a human, yep we're here, actually walks through and QAs the completed work

and then produces more tickets for the Kanban board which then goes and are executed, more QA, you get the idea.

But what do you think about this?

What did I get wrong and what am I missing here?

I imagine these phases will grow to 8 phases and 9 phases as I get more ideas.

100

There's no explicit mention of code review here really, I suppose I could do that as part of the execution flow.

101

I suppose maybe it comes under QA, but it's definitely an essential step to producing good code.

102

Either way, you can tell that I care about good code

103

and if you do too then you should check out my newsletter.

104

But whether you sign up or don't, thanks for watching and I'll see you very soon.

ऐप डाउनलोड करें

धाराप्रवाह बोलने के लिए ज़रूरी सब कुछ

AI उच्चारण स्कोरहर वाक्य का स्कोर

IPA अभ्यासहर ध्वनि में महारत

शब्दावलीअपना शब्द भंडार बनाएँ

शब्द गेमखेलते हुए सीखें

QR स्कैन करें

Download on theApp Store Get it onGoogle Play

उसी चैनल से

New Skills! v1.1 brings /wayfinder, /research, /implement, /to-spec, /to-tickets

Business EnglishUniversity StudentsWorking Adults

How to actually force Claude Code to use the right CLI (don't use CLAUDE.md)

Business EnglishUniversity StudentsWorking Adults

Most devs don't understand what agents are

Daily ConversationUniversity StudentsWorking Adults

Most devs don't understand how LLM tokens work

Business EnglishUniversity StudentsWorking Adults

New Skills! /handoff, /prototype, /review and /writing-* | Skills Changelog

Business EnglishUniversity StudentsWorking Adults

FOR YOU

अनुशंसित वीडियो

41 वाक्य

Design Thinking 101

Business EnglishUniversity StudentsWorking Adults

12 वाक्य

Rapid Engineering With AI

Business EnglishUniversity StudentsWorking Adults

204 वाक्य

The 7 Skills You Need to Build AI Agents

Business EnglishUniversity StudentsWorking Adults

241 वाक्य

AI Concepts Every Product Manager Should Know in 25 Minutes | Crash Course

Business EnglishUniversity StudentsWorking Adults

129 वाक्य

How to Become a Great Software Developer — Best Advice from Top-Notch Engineers

Business EnglishInterview EnglishUniversity Students

179 वाक्य

Understand AI in 14 minutes – with Anthropic's Chloe Lubinski [ARC 2026]

Business EnglishDaily ConversationUniversity Students

24 वाक्य

Introducing Automations: always-on coding agents

Business EnglishUniversity StudentsWorking Adults

180 वाक्य

Your API Can’t Handle Real-World Integrations

Business EnglishUniversity StudentsWorking Adults

117 वाक्य

I've Changed My Opinion On DEVELOPERS Vibe Coding

Business EnglishUniversity StudentsWorking Adults

141 वाक्य

How AI is Changing Data Scientist's Workflow

Business EnglishUniversity StudentsWorking Adults

इस पाठ के बारे में

इस पाठ में, आप ए.आई. द्वारा संचालित विकास के सात चरणों के बारे में जानेंगे। ये चरण आपकी कोडिंग प्रक्रिया को व्यवस्थित करने में मदद करेंगे, जिससे आप समझ सकेंगे कि कैसे एक विचार को निर्माण में बदलना है। आप यह भी सीखेंगे कि अनुसंधान, प्रोटोटाइपिंग, और उत्पाद आवश्यकता दस्तावेज (PRD) कैसे विकसित करना है। इन चरणों का ज्ञान आपको ए.आई. तकनीकों का प्रभावी ढंग से उपयोग करने में मदद करेगा।

मुख्य शब्दावली और वाक्यांश

चरण (Phase): किसी प्रक्रिया का एक हिस्सा।
विचार (Idea): विकास की प्रेरणा या रूपरेखा।
प्रोटोटाइप (Prototype): प्रारंभिक नमूना या मॉडल।
अनुसंधान (Research): किसी विषय पर गहराई से जानकारी इकट्ठा करना।
उत्पाद आवश्यकता दस्तावेज (PRD): अंतिम उत्पाद की विशेषताओं का विवरण।
कोडबेस (Codebase): कोड का संग्रह जो किसी प्रोजेक्ट में काम आता है।
संघटन (Organization): किसी कार्य को व्यवस्थित रूप से करना।
इम्प्लीमेंटेशन (Implementation): किसी विचार या योजना को व्यावहारिक रूप में लाना।

अभ्यास टिप्स

इस पाठ के दौरान जब आप वीडियो सुन रहे हों, तो अपने बोलने की गति को बनाए रखने के लिए शैडो स्पीच का अभ्यास करें। शैडोइंग एक प्रभावी तकनीक है, जहां आप वीडियो में बोले गए शब्दों को एक साथ बोलते हैं। इस वीडियो की गति को ध्यान में रखते हुए, नोट करें कि जब वक्ता अपने विचार साझा करता है, तो वह कैसे अपना दृष्टिकोण स्पष्ट करता है।

आप इस प्रक्रिया में यूट्यूब से अंग्रेजी सीखें और अंग्रेजी शैडोइंग का उपयोग करते हुए अपनी उच्चारण और भावनाओं को निखारना चाहेंगे। सुनिश्चित करें कि आप पहले कुछ बार सुनें और फिर अपनी आवाज में दोहराएँ। यह न केवल आपकी सुनने की क्षमता को बेहतर बनाएगा, बल्कि आपके स्पीच पैटर्न को भी सटीक बनाने में मदद करेगा।

वीडियो के दौरान भावनात्मक टोन और भावना को पकड़ने पर ध्यान दें, क्योंकि यह आपके shadowspeak को और भी प्रभाबित करने में मदद करेगा। अभ्यास करते समय, अपने आप को वीडियो की लय के साथ समन्वयित करें और जितना संभव हो सके उसी जेश्चर और उत्साह के साथ बोलने का प्रयास करें।

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	have identified · have understood · we've actually seen
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	are built · be turned · be done
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	people that are · route, which is · PRD which is

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: The 7 phases of AI-driven development - वीडियो के साथ अंग्रेजी बोलना सीखें

अभ्यास मोड चुनें

ऐप डाउनलोड करें

mattpocock/skills: A complete AI Coding workflow, end-to-end

I was an AI skeptic. Then I tried plan mode

/wayfinder: Nothing is too big to plan anymore

New Skills! v1.3 brings /pr, /implement-spec, and /retro

New Skills! v1.2 brings /wait-what, /writing-for-agents, and fixes /grill-me

Learn anything with the /teach skill

5 Claude Code skills I use every single day

Can Cursor's HARDCORE Review Skill Stop The Slop?

New Skills! v1.1 brings /wayfinder, /research, /implement, /to-spec, /to-tickets

How to actually force Claude Code to use the right CLI (don't use CLAUDE.md)

Most devs don't understand what agents are

Most devs don't understand how LLM tokens work

New Skills! /handoff, /prototype, /review and /writing-* | Skills Changelog

Design Thinking 101

Rapid Engineering With AI

The 7 Skills You Need to Build AI Agents

AI Concepts Every Product Manager Should Know in 25 Minutes | Crash Course

How to Become a Great Software Developer — Best Advice from Top-Notch Engineers

Understand AI in 14 minutes – with Anthropic's Chloe Lubinski [ARC 2026]

Introducing Automations: always-on coding agents

Your API Can’t Handle Real-World Integrations

I've Changed My Opinion On DEVELOPERS Vibe Coding

How AI is Changing Data Scientist's Workflow

इस पाठ के बारे में

मुख्य शब्दावली और वाक्यांश

अभ्यास टिप्स

इस वीडियो का व्याकरण

शैडोइंग तकनीक क्या है?