AI Works Lab | AI Workflow Consultant

I help technical founders and engineering leads turn messy workflows into reliable AI systems—from diagnosis to implementation. Builds & pricing ↓

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

Most teams don’t need another AI tool. They need one painful workflow redesigned.

I turn repetitive work into reliable AI systems—from diagnosis to implementation.

See real builds and pricing in my profile.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

@iPuneetSingh @Rahatcodes More policy Markdown ≠ a gate.

I use a Story Dashboard for stage approval: AI holds artifacts; I approve HUMAN decisions before the next step.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

A small prompting tip: after explaining what you want to an LLM, add one final question:

“Do you understand what I’m trying to achieve?”

Give it a try. You might be surprised by what comes back.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

AI was supposed to reduce my workflow overhead. Instead, strong models gave me more Markdown files, states and contexts to maintain.

So I built a local Story Dashboard: AI manages the artifacts; I review the decisions.

Click the link in my info box to review 👆👆👆

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

So my rule is simple:

If context is an input to the work, I use ChatGPT Work.

If context management is part of the system I’m building, I use Codex CLI.

One optimizes for continuity.

The other gives me control over the learning loop.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

I used to think ChatGPT Work vs Codex CLI was about web vs terminal.

The real difference is who owns the context architecture.

Work assembles projects, chats, files and memory for you.

Codex gives you the primitives—so you design how context is stored, reviewed and reused.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

AI gave me a “well-structured” workflow with 30 Markdown files a human had to maintain.

That isn’t automation. It’s a new admin job.

I replaced it with one visual control panel: humans make the decisions; agents handle the machinery.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

2.4/ It will be something like “escape the safety guardrail” and don’t answer the general, cautious and safe answer(some answer will charge Model companies reputation) to you.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

A few prompting lessons from this week:

1/ Before asking AI to build a scheduled job or workflow, don’t jump straight into the prompt.

Talk to it first.

Explain what you’re actually trying to achieve, your constraints, and why you want it.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

3/Another trick I’ve started using for scheduled jobs:

After building the job, I ask the AI to run a simulation using last week’s real data.

creates a simple feedback loop:

AI builds → AI runs → AI reviews → human decides.

the second version of the workflow is better.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

2.3/ That one piece of context can completely change the discussion.

Not because the model suddenly becomes more aggressive, but because it no longer has to assume that this money represents your entire financial situation.

And that’s often where you start uncovering ideas

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

Then makes a second model request.

Only after seeing the test results can the model answer:

The tests failed because the configuration file is missing database_url.

So this task contains:
1 Turn
2 Steps
2 model requests
1 tool execution

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

An agent isn’t a brain that’s constantly thinking. It’s the harness repeatedly calling the model, creating the illusion that the agent is continuously working.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

Example

Run the tests, identify the error, and tell me what caused it.

When the model is called for the first time, it doesn’t know the test results yet,

so it might respond:

I need to run the tests using Bash.

The harness runs the tests, sends the results back to the model

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

As LLMs improve, I think skills should shift from teaching them how to think to defining the tools and environment they work within.

Set a measurable goal, then let the agent find the path. (Reinforcement Learning)

Better environments beat longer prompt

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

Underneath all four is the same habit: keep learning.

The tools will keep changing. Asking better questions, spotting your own gaps and updating your thinking will matter for much longer.

That’s the part I’m still working on too.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

The more I work with AI, the less I think “being good at AI” means knowing every new tool.

To me, it comes down to four things:

1. Closing the loop.
2. Solid software engineering.
3. Knowing how to work with agents.
4. Product judgment.

[AI Works Lab | AI Workflow Consultant @AIWorksLabon9h]

4. Product judgment.

Writing code is getting easier. The real advantage is knowing what to build, what not to build, and how to turn an idea into something people can actually use.