there's so much content on how to build AI agents, but no one ever talks about the data engineering pipelines that support them

here's a thread going over the basics of data engineering: the goal of data engineering:

- extract data from various sources
- transform it into structured format
- load into a data warehouse like Snowflake

and this structured data often is used context for AI systems to make personalized recommendations

how to reverse engineer any successful AI product: step 1: understand the manual process

before diving into a technical analysis, figure out what human task this AI product is automating

> what would someone do manually to achieve the same result?
> what decisions need to be made?
> what data is required at each step?
> what is the most painful part of this task that people are paying to automate?

how to discover AI business ideas that actually make money (step by step breakdown): step 1: research industries with manual bottlenecks

use this prompt to understand where people are struggling:

"You're a 20-year veteran in the [INDUSTRY]. What tasks consume the most time daily? What repetitive work do you wish would disappear?"

test this across different sectors

how to vibe code your AI project in 7 days (step by step breakdown): day 1: map out your system design

don't jump straight into code - plan the complete workflow first

what steps does your tool need to get users from point A to point B?

create a document outlining each step, what data is needed, what decisions get made

how to build your first AI agent (complete roadmap): step 1: find a real problem worth solving

forget about AI for a second and think about tasks that:

> take up hours of someone's time every week
> are repetitive and monotonous
> cost the business real money when delayed
> currently require employees to do manually

the last 6 years I've built AI systems for startups, mid-sized companies & global enterprises

here are my 11 biggest lessons on building AI systems: lesson 1: shadow employees before building anything

spend time with whoever currently does the job you're trying to automate

understand the industry nuances, requirements, and how they actually envision the workflow

how to become elite at AI (step by step breakdown): step 1: learn to code

start with Python basics - for loops, data structures, classes, all that fundamental stuff

you need one programming language to understand how systems actually work

and Python is the most common language for development

here's a breakdown of an AI agent that I built for a global enterprise: the problem: their legal team was spending hundreds of hours searching through contracts

- they needed to find specific clauses
- compare terms across clients
- answer questions like "which contracts have upgrade clauses?"

all done manually

here's a full behind-the-scenes walkthrough of an AI workflow I built for a B2B SaaS: the problem: this ecommerce marketing SaaS had thousands of brands in their database

but Shopify's categories were way too broad - "apparel", "health & beauty", etc

they needed granular subcategories like "gymwear," "streetwear," "skincare" to improve their backend models

how to build an AI that analyzes your sales calls and tracks rep performance automatically: the problem with tracking sales performance:

> manually reviewing every call takes forever
> reps aren't getting consistent feedback
> you have no way to track improvement over time

how to build and deploy an AI chatbot (code included): step 1: gather your training documents

collect all the files you want your chatbot to learn from - company docs, SOPs, FAQs, PDFs, whatever

put everything in a docs folder

this becomes your chatbot's knowledge base

here's everything you need to know about how LLMs work (a beginner's guide): first understand that LLMs are prediction machines

when you type "the sky is" it predicts "blue" is the most
ikely next word

based on patterns it learned from massive amounts of text

here's everything you need to get your first paid AI project (exact roadmap): step 1: forget AI exists

I know this sounds backwards, but remember AI is just a tool to solve problems

come from the mindset with "what expensive problem can I solve?" then figure out if AI can help

here's a simple framework that makes any AI workflow 10x more reliable: stop trying to do everything in one call

you're killing your output quality

when you ask ChatGPT to "research ad angles, write scripts, and generate videos" all at once, every single step comes out diluted

the solution is breaking it into separate calls and chaining them together

most people are overwhelmed by the amount AI tools coming out everyday

here's a simple framework to pick only the tools that you should focus on: most people rn are drowning in AI tools and switching back and forth

they see everyone on Twitter posting about the latest workflow or this new tool that will change their life

all it does is overwhelm them even more

a step-by-step breakdown how to train a machine learning model (so that you can give better context to an AI): I'm going to walk you through building a real ML model I created for a startup

the goal: detect risky borrowers who might not pay back loans

this will teach you how data and context work, which directly applies to getting better outputs from ChatGPT and Claude

here's everything you need to vibe code your AI project: people hit the same few problems when vibe coding

> your build is a broken mess because you didn't plan properly
> one small change breaks 50 other things
> your app has zero security and gets hacked
> you learned nothing and can't fix anything when it breaks

here's how to avoid all that

here's the one secret that gives any AI system an unfair advantage over everyone else: everyone uses the same AI models (ChatGPT, Claude, etc.)

so why do some AI systems work amazingly while others are completely useless?

the difference isn't the AI - it's the data you build around it

here's everything you need to build a team of AI agents: most people try to build one AI agent that handles everything all at once

but that's like hiring one person to do research, sales, marketing and customer service

and you end up with mid results across all the tasks

everything you need to automate customer research with AI: manual customer research takes forever and you're probably missing the best insights

reddit is a goldmine of real customer conversations

where people share their actual problems, desires, and frustrations

but manually going through threads every day isn't scalable

here's everything you need to write prompts that actually work: most people think these AI models are absolute superhumans

like they're 5000 IQ and can read your mind

yeah, they're smart af, but here's what everyone misses:

even the smartest AI models needs to be managed like a new employee