Welcome to the Deep Dive. Today, we're aiming
to really help transform your coding workflow.
We're not just scratching the surface of chat
GPT. We're going deep, looking at how you can
actually harness its power for complex development
tasks. Our goal, give you a shortcut basically
to understanding how AI can be a serious copilot,
saving you time, boosting your work quality.
Our insights today, they're drawn from advanced
chat GPT prompts for devs, optimized coding by
Neil Phan. That just came out June 19th, 2025.
Very recent, yeah. So get ready. We're about
to unpack some really specific, actionable strategies.
Everything from automation to, well, designing
whole system architectures. Okay, let's unpack
this. Well, what's really fascinating here, I
think, is it's not just about the tools. It's
the sheer cognitive load, right? The repetitive
stuff developers deal with every day. Totally.
And this deep dive, it tackles that head on.
The article isn't just, you know, give me a code
snippet. It's about getting high quality actionable
results actionable across what is it 15 key development
areas we're talking real benefits saving hours
sure but also improving code quality maybe even
solving problems you're kind of stuck on right
now okay so let's kick things off with something
familiar setting up CI CD pipelines from scratch.
That's often a big headache, isn't it? Oh, absolutely.
It's a huge pain point. Is AI genuinely a game
changer here, or is it just like a nice to have
tool? No, I'd say it's pretty much a game changer,
especially for that initial setup. We've all
spent, you know, hours wrangling with CircleCI
or Render or whatever platform, trying to get
that ComSig just right. The source shows how
these advanced prompts can build a complete pipeline.
Not just the config file, but setup guides too.
Okay. The whole point is automating in the build,
test, deploy cycle. You just tell it about your
project tech stack, repo, where it's deployed,
security needs. And then what you want the pipeline
to actually do, build, test, security check,
deploy, maybe documentation. And the example
they use is super practical. A Python app, Django,
PostgreSQL, Bitbucket, deploying to render, needs
a safety check. And boom, out comes a CircleCI
YAML file, ready to go. It's almost like having
an expert right there doing it for you. Exactly.
And think about it. It's not just saving your
hours. It changes the team's agility. Right.
What if you free up a whole day, every month,
for every developer? What could your team build
with that extra time? That's the real shift.
Faster cycles, better testing, clear docs. OK.
So sticking with workflow, once CI -CD is smooth,
the next tangle is off in Git, right? Messy histories,
branch conflicts, nightmare fuel. Sometimes.
Uh -huh. We've all been there. How does AI help
untangle that mess? Yeah, we've all stared at
merge conflicts late at night wishing for a magic
wand. AI isn't magic, but it gets pretty darn
close for this stuff. Okay. ChatGPT can actually
look at your repository state, suggest the steps
to fix things, and give you the precise Git commands.
Wow. It's designed for those tricky bits, resolving
conflicts, cleaning up history. You describe
the situation, your platform, say GitLab and
what you want to achieve. Merge, squash commits,
whatever. Right. So imagine you've got conflicts
between, like, feature payment and main on GitLab.
And feature payment has a ton of messy commits.
Yeah, it happens all the time. Chat GPT guides
you through the exact sequence. Get checkout,
get fetch, get merge. Then it tells you how to
spot the conflicts. Manually fix them, add commit.
Step by step. Then the commands to squash those
messy commits into one clean one. Get reset soft.
Get commit -desham. Then push. check out main,
pull, merge main, push main. It's like having
a Git expert whispering in your ear. That's really
useful. And just to mention for anyone listening,
we'll put that full command list in the show
notes so you can just copy paste. Perfect. So
it resolves conflicts, cleans up history efficiently,
gives you the right Git commands, really helps
manage your repo, especially on something like
GitLab. Exactly. So what does this really mean
day to day? Less time fighting Git, more time
actually coding. Right. And once your Git is
clean and CICD is smooth, You're building faster.
But is that fast code also performant code? Ah,
good point. Finding and fixing inefficient code.
That's another common, often frustrating challenge.
It can really kill the user experience. Definitely.
And this is where these advanced prompts can
really make a difference. They analyze your code,
find performance bottlenecks, and suggest specific
optimizations. The goal is tackling things like
slow database queries. You give it the language,
the code snippet, the environment, Node .js on
Vercell, maybe, and the problem, like a slow
API response. The source uses a great example.
A user's API in Node, using SuperBase on Vercell,
taking like Five seconds plus for 10k record
just because of a basic select. Yeah, that's
bad. So what does it suggest? Well, the optimizations
are super actionable. First, limit columns and
rows. Use specific super base methods like select
ID, name, email, dot range. That cuts down data
transfer big time. OK, it makes sense. Second,
add an index to the users table. Like create
index, index user's name, on user's name, speeds
up searches, filters, basic stuff, but easy to
forget. And third, caching. Use Redis, maybe.
Cache those query results for an hour. That drastically
cuts down, hits to SuperBase, gets response time
under, say, 100 meters. Wow. From over five seconds
to under 100 meters. That's huge. It really is.
You get accurate, ready -to -use optimization
code. Perfect for bigger apps, especially on
Vercel with SuperBase. It makes you think, right,
what part of your app could use a performance
check like this? Exactly. Food for thought. OK,
moving on. Unit tests. We know they're vital.
Catch bugs early, ensure code works. But writing
them, especially covering all the edge cases,
that takes time. A lot of time. Oh, yeah. It
can feel like a drag sometimes, even though it's
important. So how detailed can AI get here? Can
it really handle the tricky edge cases? Surprisingly
detailed. That's why these prompts are so valuable
here. They generate comprehensive test cases.
The whole idea is writing unit tests for a specific
function or module. You just give it the language,
the function name, the testing library. Jest
is the example here. And the prompt asks for
at least three test cases. Normal, edge, edge.
So think about a calculated discount function
in Node .js using Jest. The output gives you
Specific tests, normal case, calculated discount
120, returns 80. Makes sense. Edge case, 0 %
discount, calculated discount 50, returns 50.
Still simple. But then the important one, invalid
inputs. Calling it with 100, 220, should throw
an invalid input error. Ah, catching those type
errors. Crucial. Exactly. It ensures robust coverage
and does it efficiently. So. You get unit tests
generated fast, covering key scenarios, including
those vital edge cases, accurate just code, boost
reliability in your node projects. That's a win.
Okay, so we've got efficient code, good tests,
solid building blocks. But how do they all fit
together? System architecture, that feels like
a big one. Crafting those design templates takes
serious effort. It really does. It's often a
very involved process. How much heavy lifting
can AI actually do for architecture design? Quite
a bit, actually. More than you might think. The
source shows prompts generating full architecture
designs, diagrams, technologies, the work. Really?
Full designs? Yeah. The idea is create a whole
template. You feed it system info type, requirements,
tech stack. It proposes an architecture, lists
key components, how they interact. and gives
you a diagram. A diagram too? How? Using PlantUML
syntax. It clever describes diagrams in simple
text. So you can edit it, version control it
like code. Huh, PlantUML. Okay. So the example
is an online learning app on Vercel, needs 5K
concurrent users, secure video, payments, Node
.js, React, SuperBase. Right. The prompt suggests
microservices. details the components, API gateway,
user service, video, quiz, payment services,
and the plant UAML shows users hitting React,
going via the gateway to the services, SuperBase,
Stripe. Wow. That generates clear designs fast
and editable diagrams you can stick right in
your docs. It seems perfect for that kind of
app on Vercel SuperBase. It's a fantastic starting
point. I have to ask. Are there pitfalls? Can
you just trust an AI -generated architecture
or does it sometimes miss things? That's a really
important question. It's brilliant for getting
that initial blueprint, that structure, but no,
I wouldn't just blindly deploy it. You absolutely
need human expertise to validate it. Does it
really meet subtle business needs? Unique scaling
issues, specific security policies your company
has. Right, the nuances. Exactly. Think of it
as a super smart co -pilot, not the autopilot.
It builds the scaffolding fast so you can focus
on the critical human refinement. That makes
perfect sense. A powerful assistant, not a replacement.
OK, speaking of complex stuff. Yeah. Integrating
third party APIs. Always fun, right? Yeah. Authentication,
error handling, performance. Yeah, it could be
intricate. Lots of documentation reading. So
how do prompts help here? Well, the source explains
they can generate the integration code itself,
including robust error handling and performance
optimizations. OK. The goal is just streamlining
it. Give it your project info, API name, what
it does, your tech stack. It spits out integration
code, handles common errors, suggests things
like caching or rate limiting. Gotcha. The example
is Stripe payments in a Node .js app. Express
SuperBase on Vercell. Pretty standard setup.
Right. And the generated code includes the Stripe
payment intents .create call plus updating the
order status in SuperBase. Nice. But crucially,
it shows error handling. Catching specific Stripe
errors like card declined or invalid API key.
That's key. Handling errors properly. Definitely.
And for optimization? It uses express rate limit
to cap request to the payment endpoint, prevents
abuse, keeps things stable. So faster, safer,
Stripe integration, clear error handling, performance
considered, ideal for node apps on Verzal. Exactly.
Imagine integrating multiple services that efficiently.
Cuts down research time massively. Yeah, no kidding.
And that connects right into automating other
repetitive tasks, things like backups, maybe
deployments, monitoring, essential, but... Tedious.
Duper tedious. The kind of thing everyone puts
off. Can prompts generate scripts for that too?
Yes, absolutely. Complete automation scripts
with error handling and optimization baked in.
Okay. The goal is just creating an efficient
script for your specific task. Describe the task,
platform OS, tools, services involved, and your
preferred language, like Bash or Python. Right.
The practical requirements specify needing good
error handling and maybe security optimizations
like encrypting backup files. Smart. The example
given is backing up a super base database. Yeah.
which is Postgres, right, to AWS S3, using a
bash script on Linux with the AWS and SudoBase
CLIs. OK, walk me through that script logic.
It's pretty solid. It uses PGDump to export the
database, then gzip to compress it, then GPG.
with AES -256 encryption using a passphrase for
security. License secure. Then AUS S3CP to upload
the encrypted compressed file to S3. It also
handles creating directories, checks for errors
at each step, and cleans up temporary files afterward.
a full secure solution. So automated, secure,
efficient backups, concise script, clear error
handling, perfect for SuperBase and S3 users,
takes a critical chore off your plate. Exactly,
frees you up. Okay, let's shift from operations
back to planning. That very first step, analyzing
user requirements. They can be so vague sometimes.
Oh, tell me about it. Make it like Facebook,
but for dogs, you know. Uh -huh, exactly. Trying
to hit a moving target in fog, so are you saying
AI can actually cut through that fog, take vague
requirements and give us specs? It really can
help clarify things significantly. Advanced prompts
analyze those requirements, pull out key features,
and create concrete specifications. How does
that work? Well, the purpose is to take that
fuzzy initial description, figure out the audience,
the tech stack. Then the prompt asks for primary
secondary features, detailed specs with constraints,
and even suggests clarifying questions to ask
the user. The example is a task management app
for office remote workers. Node, React, SuperBase.
Users want, create, track, complete tasks, get
reminders. Pretty standard. The AI analysis breaks
it down. Primary features, create, track, reminders,
secondary, edit, delete, filter. Then the specs
get detailed. Constraints like, store in SuperBase,
1K tasks user limit. Load max 50 tasks at once.
React UI. Use SendGrid 100 emails day limit.
Wow, specific constraints. And crucially, it
suggests questions. Needs user login. Which auth
methods? Customizable reminder timing. Task edit
history needed. Things you might forget to ask
initially. That's fantastic. Turns vague into
actionable specs. Saves huge analysis time, especially
for apps on Versel. And those clarifying questions
up front. Gold. Prevents rework later. Precisely.
Get it right early. And that precision, does
it extend to user documentation? Because docs
often feel like the last thing anyone wants to
do. Right. Often rushed or skipped entirely.
Is AI the magic bullet for doc writing, making
it less painful? Well, maybe not magic, but it
definitely helps. The value of clear guides is
huge. But yeah, it's time consuming. So what
can prompts do? They can generate full user documentation.
Yeah. Usage guides, FAQs, tips. The goal is just
creating accessible docs. OK. the app name features
audience. The prompt asks for guides for key
features and FAQ with common questions, maybe
a couple of tips. Example, a task app called
TaskEasy. TaskEasy got it. It generates step
-by -step guides, creating tasks, receiving reminders,
and FAQ covering things like filtering completed
tasks, tasks without due dates, troubleshooting
emails, and tips like prioritize tasks, use filters
effectively. That sounds really comprehensive,
everything a new user needs. Pretty much. Get
some started, answers common questions. So user
-friendly docs, FAQs, tips enhance the experience,
suits an app like TaskEasy on Vercel perfectly,
frees up devs to build, not just explain, huge
time saver. Definitely. Yeah. Now let's talk
about something absolutely critical, security.
Ah, yes. Can't forget security. No matter how
cool your features are, if it's insecure, it's,
well, useless or dangerous. True. So, props can
help analyze security, too, and suggest fixes
with code. Exactly. The source shows they can
analyze your app, suggest enhancements, and provide
the code to implement them. How does that work?
The purpose is optimizing security. You give
it app type, tech stack, any known concerns.
It identifies vulnerabilities. Maybe three key
ones suggest fixes with code or config, and explains
why. OK, example. Optimizing a Node .js REST
API for that task app on Vercel. Let's say it
initially has no user auth or API protection.
Big problem. Yeah, wide open. The analysis pinpoints.
Lack of user auth. SQL injection risk. Missing
HTTP security headers. Pretty standard vulnerabilities.
And the fixes. Concrete code. For off .nojs middleware
using JSON web token JWT to protect routes. For
SQL injection, explain SuperPace's automatic
parameterization, protect you there, good to
know. That's handy. And for headers, using the
helmet middleware and express, maybe with content
security policy, to block XSS and other attacks.
So it rapidly strengthens API security, gives
you actual usable code, ideal for node apps on
Vercel. Proactive defense, not waiting for a
breach? Exactly. Much better approach. OK, nearly
there. Last couple of points. Performance reports.
Collecting and presenting that data can be a
real chore. Yeah, pulling metrics from monitoring
tools, formatting reports, it's often manual
and tedious, like a scavenger hunt sometimes.
So prompts simplify the scavenger hunt. That's
the idea. They create the performance queries
and the report templates. The purpose is generating
a concise report. Give it app info type, monitoring
tool, like Prometheus, metrics you care about.
It suggests metrics, gives you the exact queries
to get the data, and formats the template. Example.
Node .js REST API using Prometheus. Tracking
response time, error rate, throughput. Standard
metrics. What about the queries? It provides
the precise PromQL queries. For P95 response
time, error rate percentage, request throughput
per endpoint. No more guessing the syntax. Oh,
that's huge. Getting PromQL right can be tricky.
Tell me about it. Then it generates a clear markdown
table template. Columns for endpoint, response
time, error rate, throughput, maybe some example
data. So, concise, readable reports, accurate
Prometheus queries, ready to paste, helps you
optimize, especially for apps on Vercel, actionable
insights right there, less time measuring, more
time improving. Exactly, focus on making things
faster. Okay, last one, tying it all together.
Okay. The software test plan, essential for meeting
requirements, finding bugs early, but creating
one, big task. Huge task. often feels bureaucratic,
but is really important for quality. And prompts
can generate these plans, too, including scenarios,
scope, criteria. Yes. The purpose is streamlining
your testing strategy. Describe the app, features,
text stack. You define scope, what's in, what's
out. Suggest test scenarios, including edge cases.
And create a structured test plan table with
success criteria. OK, like for our task, EasyRest
API on Vercel. Right. Scope. Test create. edit,
delete tasks, reminder notifications, test types,
functional, integration, performance, security,
exclusions, detailed UI testing, mobile testing,
make sense, test scenarios, create tasks with
valid inputs, edgecases .max .link, pass due
date, test reminders, edgecases .no due date,
near due time, test deleting tasks with permissions,
edge cases, on -off user, nonexistent tasks.
That was a lot of ground. And the table lists
it all out clearly. Test ID features scenario
type success criterion, like T1 create test,
valid input. Functional tasks saved in SuperBase
returns 201 status. Or T6 delete task. Another
user's task security returns 401, unauthorized.
That's a really clear, actionable test plan.
Covers functional, security, integration. Perfect
for apps on Vercell SuperBase. Helps catch bugs
before they escape. Exactly. Builds confidence
in your releases. Wow. OK, what an incredible
deep dive that was. We've really seen how chat
GPT can shift from just a coding helper to a,
well, a genuine development partner. Yeah, it
covers a lot. We hit so many areas, automating
CICD, architecture analysis, performance tuning,
generating tests, security hardening, documentation.
And these prompts, they're not just theory. They
offer practical, actionable solutions. Whether
you're building on Drussel, using SuperBase,
or just want to streamline your day -to -day.
And that really leads to the key takeaway, I
think. It raises an important question for everyone
listening. What complex, time -consuming task
in your development process could you transform
next? Right. By applying these kinds of advanced
AI prompting strategies, I really encourage you,
grab one of these ideas, copy a prompt example,
paste it into ChatGPT, tweak it for your needs,
and just see what happens. Experience that transformation
yourself. Absolutely. That's all for this deep
dive. Go experiment, keep learning, and see how
AI can boost your workflow.