u/Worried-Variety3397

Maybe I’m doing this the dumb way, but I swear this keeps happening

I can get the LLM/agent part working pretty fast. Then the actual docs show up and now I’m screwing around with OCR, parsers, chunking, metadata, validation, weird PDFs, etc.

Feels like half the work has nothing to do with the LLM lol

I’ve been hacking on a way around it:

Drop in the raw stuff, say in normal English what you’re trying to do with it and what you want back, then let it handle the cleanup / chunking / tagging / validation.

Like:

“these are support docs, chunk them by section, keep the product + version metadata, flag anything sketchy, and give me clean JSON for RAG”

That’s basically the whole idea

Are you guys building this crap from scratch every time too, or is there a better way you’ve landed on?

I keep running into this with RAG projects.

Getting the basic retrieval pipeline working is usually pretty straightforward.

Then the real docs show up.

PDFs, scanned files, spreadsheets, emails, weird layouts, duplicate junk... and suddenly you’re stitching together OCR, parsers, chunking logic, LLM calls, metadata extraction, regex and validation just to get something worth indexing.

I’ve been messing around with a simpler approach:

raw files → pick the task → describe in plain English how you want the data handled + what you want back → get RAG-ready data

Things like:

clean/prep 、 chunk 、 generate metadata/tags 、 generate Q&A pairs

For example:

messy PDFs → clean the content → chunk by section → add metadata → validate → vector DB

Basically, describe the end result instead of building the whole pipeline yourself.

Anyone else dealing with this?

How are you handling it right now?

If anyone’s interested, I’d be happy to let you try it for free.

Been running into this a lot lately.

Building the actual automation in n8n/Make/Zapier is usually the easy part.

Then some random PDF, email attachment, spreadsheet, scanned doc, whatever shows up and suddenly half the workflow is OCR + LLM calls + regex + code + random APIs just to turn the thing into usable data lol.

So I’ve been messing around with a different approach.

Basically:

throw in the raw files → tell it what you want out → get back cleaned/structured/validated data

No need to map out the whole pipeline. Just tell it what you’re trying to do with the data and what you want the end result to look like.

Like:

email attachment → customer info → clean JSON → CRM

Instead of building all the data plumbing yourself.

Not sure if this is just something I keep running into or if it’s a pretty common pain in no-code stuff.

How are you guys dealing with messy inputs right now?

If anyone has an ugly real-world example, send it my way. Would actually love to test this against the annoying stuff people are dealing with.

Hey folks, I keep running into the same annoying problem with n8n.

You can vibe code and build some pretty legit workflows without being a data engineer. Then you hit the data part and suddenly you’re duct taping together OCR, LLM calls, regex, code nodes, validation rules, random APIs, etc. just to get the input into a usable shape.

I’ve been messing around with a simpler idea:

Drop in raw stuff like PDFs, email attachments, spreadsheets or CSVs, tell it what you want back, and it handles the parsing, cleanup, structuring and validation.

Something like:

Email attachment → extract → clean → validate → CRM ready JSON

Basically, you describe the end result instead of building the whole data pipeline yourself.

Curious if anyone else runs into this.

How are you guys handling this stuff right now?

Also happy to let a few people mess around with it if anyone wants to test it with me.

I’ve been looking closely at how people use n8n to turn emails and attachments into usable CRM or database records.

The typical setup seems to involve a PDF parser or OCR tool, an LLM, a few Code nodes, validation rules, and sometimes a manual review step.

Extracting text from a document isn’t really the hard part anymore.

There are already plenty of tools for OCR, parsing, schema validation, LLM extraction, and human approval. The difficult part is tying all of those pieces together into a controlled process and making sure the final record is actually usable.

I’m building a tool to handle that middle layer.

You send it the email, attachments, and the JSON schema you need. It extracts and standardizes the data, checks for missing or conflicting values, and sends uncertain fields for human review before anything is written to the CRM.

The goal isn’t to build yet another PDF parser. It’s to reduce the custom parsing, cleaning, validation, and exception handling that gets rebuilt in almost every document-based workflow.

For developers building n8n workflows for real clients, would this solve a genuine problem for you, or are your current tools already good enough?

I’m also looking for a few developers who’d be willing to test it on a real workflow and give honest feedback.

I’ve been looking closely at how people use n8n to turn emails and attachments into usable CRM or database records.

The typical setup seems to involve a PDF parser or OCR tool, an LLM, a few Code nodes, validation rules, and sometimes a manual review step.

Extracting text from a document isn’t really the hard part anymore.

There are already plenty of tools for OCR, parsing, schema validation, LLM extraction, and human approval. The difficult part is tying all of those pieces together into a controlled process and making sure the final record is actually usable.

I’m building a tool to handle that middle layer.

You send it the email, attachments, and the JSON schema you need. It extracts and standardizes the data, checks for missing or conflicting values, and sends uncertain fields for human review before anything is written to the CRM.

The goal isn’t to build yet another PDF parser. It’s to reduce the custom parsing, cleaning, validation, and exception handling that gets rebuilt in almost every document-based workflow.

For developers building n8n workflows for real clients, would this solve a genuine problem for you, or are your current tools already good enough?

I’m also looking for a few developers who’d be willing to test it on a real workflow and give honest feedback.

I’ve been looking closely at how people use n8n to turn emails and attachments into usable CRM or database records.

The typical setup seems to involve a PDF parser or OCR tool, an LLM, a few Code nodes, validation rules, and sometimes a manual review step.

Extracting text from a document isn’t really the hard part anymore.

There are already plenty of tools for OCR, parsing, schema validation, LLM extraction, and human approval. The difficult part is tying all of those pieces together into a controlled process and making sure the final record is actually usable.

I’m building a tool to handle that middle layer.

You send it the email, attachments, and the JSON schema you need. It extracts and standardizes the data, checks for missing or conflicting values, and sends uncertain fields for human review before anything is written to the CRM.

The goal isn’t to build yet another PDF parser. It’s to reduce the custom parsing, cleaning, validation, and exception handling that gets rebuilt in almost every document-based workflow.

For developers building n8n workflows for real clients, would this solve a genuine problem for you, or are your current tools already good enough?

I’m also looking for a few developers who’d be willing to test it on a real workflow and give honest feedback.

I’ve been looking closely at how people use n8n to turn emails and attachments into usable CRM or database records.

The typical setup seems to involve a PDF parser or OCR tool, an LLM, a few Code nodes, validation rules, and sometimes a manual review step.

Extracting text from a document isn’t really the hard part anymore.

There are already plenty of tools for OCR, parsing, schema validation, LLM extraction, and human approval. The difficult part is tying all of those pieces together into a controlled process and making sure the final record is actually usable.

I’m building a tool to handle that middle layer.

You send it the email, attachments, and the JSON schema you need. It extracts and standardizes the data, checks for missing or conflicting values, and sends uncertain fields for human review before anything is written to the CRM.

The goal isn’t to build yet another PDF parser. It’s to reduce the custom parsing, cleaning, validation, and exception handling that gets rebuilt in almost every document-based workflow.

For developers building n8n workflows for real clients, would this solve a genuine problem for you, or are your current tools already good enough?

I’ve been looking closely at how people use n8n to turn emails and attachments into usable CRM or database records.

The typical setup seems to involve a PDF parser or OCR tool, an LLM, a few Code nodes, validation rules, and sometimes a manual review step.

Extracting text from a document isn’t really the hard part anymore.

There are already plenty of tools for OCR, parsing, schema validation, LLM extraction, and human approval. The difficult part is tying all of those pieces together into a controlled process and making sure the final record is actually usable.

I’m building a tool to handle that middle layer.

You send it the email, attachments, and the JSON schema you need. It extracts and standardizes the data, checks for missing or conflicting values, and sends uncertain fields for human review before anything is written to the CRM.

The goal isn’t to build yet another PDF parser. It’s to reduce the custom parsing, cleaning, validation, and exception handling that gets rebuilt in almost every document-based workflow.

For developers building n8n workflows for real clients, would this solve a genuine problem for you, or are your current tools already good enough?

I’m also looking for a few developers who’d be willing to test it on a real workflow and give honest feedback.

I’ve been looking closely at how people use n8n to turn emails and attachments into usable CRM or database records.

The typical setup seems to involve a PDF parser or OCR tool, an LLM, a few Code nodes, validation rules, and sometimes a manual review step.

Extracting text from a document isn’t really the hard part anymore.

There are already plenty of tools for OCR, parsing, schema validation, LLM extraction, and human approval. The difficult part is tying all of those pieces together into a controlled process and making sure the final record is actually usable.

I’m building a tool to handle that middle layer.

You send it the email, attachments, and the JSON schema you need. It extracts and standardizes the data, checks for missing or conflicting values, and sends uncertain fields for human review before anything is written to the CRM.

The goal isn’t to build yet another PDF parser. It’s to reduce the custom parsing, cleaning, validation, and exception handling that gets rebuilt in almost every document-based workflow.

For developers building n8n workflows for real clients, would this solve a genuine problem for you, or are your current tools already good enough?

I’m also looking for a few developers who’d be willing to test it on a real workflow and give honest feedback.

I’d like to try a slightly unusual challenge.

As a data engineer, I spend a lot of time looking through messy docs, RAG setups, project knowledge bases, and AI workflows that technically “work”.

Yet the AI still forgets things, ignores rules, gives inconsistent answers, or burns through context for no obvious reason.

So here’s my plan:

I’m going to pick five AI projects from people I don’t know and diagnose and fix their knowledge base issues for free.

Could be:

• A RAG project
• A ChatGPT or Claude project knowledge base
• Vibe Coding docs and project rules
• An internal AI assistant
• Documents being fed into an AI workflow
• A deployed AI app or chatbot

I’m looking for those frustrating situations where you think:

“I already told the AI this. Why is it making the same mistake again?”

Or:

“I gave it all the documentation. Why is the answer still wrong?”

Send me a message or leave a comment with your project and the issue you’re facing.

Please don’t send confidential company data or sensitive information.

I’ll pick five interesting cases.

Let’s figure out what’s actually going wrong.

And I genuinely welcome feedback and corrections from people with experience in this space.

I’d like to launch a special challenge.

As a data engineer, I spend a lot of time digging through messy documentation, RAG architectures, project knowledge bases, and various AI workflows—systems that technically "work," yet.

AI still forgets information, ignores established rules, gives contradictory answers, or inexplicably burns through its context window.

So, here’s my plan:

I’m going to select AI projects from five people I don’t know and diagnose—and fix—the knowledge base issues for free.

These projects could be:

• A RAG project

• A project knowledge base built on ChatGPT or Claude

• "Vibe Coding"-style documentation and project rules

• An internal AI assistant

• A collection of documents ready to be fed into an AI workflow

• A web-deployed AI application or chatbot

I’m specifically looking for those maddening, baffling scenarios where you think:

"I’ve already told the AI ​​this—why does it keep making the same mistake?"

Or:

"I’ve fed it all the documentation—why is the answer still wrong?"

Please send me a private message or leave a comment telling me about your project and the issues you’re facing.

Please do not send any confidential company data or sensitive information.

I’ll select five interesting cases from the submissions.

Let’s figure out exactly what’s going wrong with these projects together. I also welcome feedback and corrections from experts as we work to solve these problems.

I’d like to launch a special challenge.

As a data engineer, I spend a lot of time digging through messy documentation, RAG architectures, project knowledge bases, and various AI workflows—systems that technically "work," yet.

AI still forgets information, ignores established rules, gives contradictory answers, or inexplicably burns through its context window.

So, here’s my plan:

I’m going to select AI projects from five people I don’t know and diagnose—and fix—the knowledge base issues for free.

These projects could be:

• A RAG project

• A project knowledge base built on ChatGPT or Claude

• "Vibe Coding"-style documentation and project rules

• An internal AI assistant

• A collection of documents ready to be fed into an AI workflow

• A web-deployed AI application or chatbot

I’m specifically looking for those maddening, baffling scenarios where you think:

"I’ve already told the AI ​​this—why does it keep making the same mistake?"

Or:

"I’ve fed it all the documentation—why is the answer still wrong?"

Please send me a private message or leave a comment telling me about your project and the issues you’re facing.

Please do not send any confidential company data or sensitive information.

I’ll select five interesting cases from the submissions.

Let’s figure out exactly what’s going wrong with these projects together. I also welcome feedback and corrections from experts as we work to solve these problems.

I keep seeing the same problem with AI projects.

The knowledge base looks fine. The docs are there. The RAG pipeline technically works.

But the AI still forgets rules, pulls the wrong context, gives inconsistent answers, or somehow burns through a ridiculous number of tokens.

I'm a data engineer and I've spent a lot of time looking at messy documents, project knowledge bases and RAG setups, so I've started paying more attention to why this keeps happening.

A lot of the time, the problem isn't the model itself. It's somewhere in the way the knowledge is written, split up, indexed or retrieved.

So if you're dealing with something like:

• “I literally told the AI this already.”
• “Why is it reading the wrong section?”
• “It has all my docs. Why is the answer still wrong?”
• “Why is this thing burning through so many API tokens?”

Feel free to describe what you're building and what's going wrong.

I'm happy to take a look, ask a few questions and share what I'd check first.

Just don't post any private or sensitive data obviously.

I'll pick 10 interesting ones.

Let's see how broken they are.

I keep seeing the same problem with AI projects.

The knowledge base looks fine. The docs are there. The RAG pipeline technically works.

But the AI still forgets rules, pulls the wrong context, gives inconsistent answers, or somehow burns through a ridiculous number of tokens.

I'm a data engineer and I've spent a lot of time looking at messy documents, project knowledge bases and RAG setups, so I've started paying more attention to why this keeps happening.

A lot of the time, the problem isn't the model itself. It's somewhere in the way the knowledge is written, split up, indexed or retrieved.

So if you're dealing with something like:

• “I literally told the AI this already.”
• “Why is it reading the wrong section?”
• “It has all my docs. Why is the answer still wrong?”
• “Why is this thing burning through so many API tokens?”

Feel free to describe what you're building and what's going wrong.

I'm happy to take a look, ask a few questions and share what I'd check first.

Just don't post any private or sensitive data obviously.

Bit of a weird challenge.

I'm a data engineer and I spend a stupid amount of time looking at messy documents, RAG setups, project knowledge bases and AI workflows that technically "work"...

but somehow the AI still forgets things, ignores rules, gives inconsistent answers or burns through context for no obvious reason.

So I'm going to try something.

I'll take 10 strangers' AI projects and try to diagnose and fix the knowledge base for free.

Could be:

• a RAG project
• a ChatGPT / Claude project knowledge base
• vibe coding docs and project rules
• an internal AI assistant
• a folder of docs you're feeding into an AI workflow

I'm mainly looking for cases where you're thinking:

"I literally told the AI this already. Why is it doing it again?"

or

"Why does this thing have all my documents and still give me the wrong answer?"

Send me a message or comment with what you're building and what's going wrong.

Please don't send confidential company data or anything sensitive.

I'll pick 10 interesting ones.

Let's see how broken they are.

Bit of a weird challenge.

I'm a data engineer and I spend a stupid amount of time looking at messy documents, RAG setups, project knowledge bases and AI workflows that technically "work"...

but somehow the AI still forgets things, ignores rules, gives inconsistent answers or burns through context for no obvious reason.

So I'm going to try something.

I'll take 10 strangers' AI projects and try to diagnose and fix the knowledge base for free.

Could be:

• a RAG project
• a ChatGPT / Claude project knowledge base
• vibe coding docs and project rules
• an internal AI assistant
• a folder of docs you're feeding into an AI workflow

I'm mainly looking for cases where you're thinking:

"I literally told the AI this already. Why is it doing it again?"

or

"Why does this thing have all my documents and still give me the wrong answer?"

Send me a message or comment with what you're building and what's going wrong.

Please don't send confidential company data or anything sensitive.

I'll pick 10 interesting ones.

Let's see how broken they are.

I keep seeing the same problem with AI projects.

The knowledge base looks fine. The docs are there. The RAG pipeline technically works.

But the AI still forgets rules, pulls the wrong context, gives inconsistent answers, or somehow burns through a ridiculous number of tokens.

I'm a data engineer and I've spent a lot of time looking at messy documents, project knowledge bases and RAG setups, so I've started paying more attention to why this keeps happening.

A lot of the time, the problem isn't the model itself. It's somewhere in the way the knowledge is written, split up, indexed or retrieved.

So if you're dealing with something like:

• “I literally told the AI this already.”
• “Why is it reading the wrong section?”
• “It has all my docs. Why is the answer still wrong?”
• “Why is this thing burning through so many API tokens?”

Feel free to describe what you're building and what's going wrong.

I'm happy to take a look, ask a few questions and share what I'd check first.

Just don't post any private or sensitive data obviously.

I keep seeing the same problem with AI projects.

The knowledge base looks fine. The docs are there. The RAG pipeline technically works.

But the AI still forgets rules, pulls the wrong context, gives inconsistent answers, or somehow burns through a ridiculous number of tokens.

I'm a data engineer and I've spent a lot of time looking at messy documents, project knowledge bases and RAG setups, so I've started paying more attention to why this keeps happening.

A lot of the time, the problem isn't the model itself. It's somewhere in the way the knowledge is written, split up, indexed or retrieved.

So if you're dealing with something like:

• “I literally told the AI this already.”
• “Why is it reading the wrong section?”
• “It has all my docs. Why is the answer still wrong?”
• “Why is this thing burning through so many API tokens?”

Feel free to describe what you're building and what's going wrong.

I'm happy to take a look, ask a few questions and share what I'd check first.

Just don't post any private or sensitive data obviously.

I keep seeing the same problem with AI projects.

The knowledge base looks fine. The docs are there. The RAG pipeline technically works.

But the AI still forgets rules, pulls the wrong context, gives inconsistent answers, or somehow burns through a ridiculous number of tokens.

I'm a data engineer and I've spent a lot of time looking at messy documents, project knowledge bases and RAG setups, so I've started paying more attention to why this keeps happening.

A lot of the time, the problem isn't the model itself. It's somewhere in the way the knowledge is written, split up, indexed or retrieved.

So if you're dealing with something like:

• “I literally told the AI this already.”
• “Why is it reading the wrong section?”
• “It has all my docs. Why is the answer still wrong?”
• “Why is this thing burning through so many API tokens?”

Feel free to describe what you're building and what's going wrong.

I'm happy to take a look, ask a few questions and share what I'd check first.

Just don't post any private or sensitive data obviously.