In today’s edition, I will break RAG down and show you why it matters, and give you a practical way to think about RAG for your work.

Today’s edition is based on my session at AI Engineer HQ

In today’s edition:

• AI Deep Dive— RAG 101 for Enterprise

• Build Together— Here’s How I Can Help You

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[AI Deep Dive]

RAG 101 for Enterprise

If you’ve been following the AI world, you’ve probably seen the term RAG thrown around a lot.

It may sound technical, but the idea is simple, and it’s one of the most significant shifts in how enterprises utilize AI today.

The Problem

Every company has more data than anyone can handle:

• structured data like CRMs, ERPs, SQL databases

• unstructured data like word docs, PDFs, wikis, emails, slack/discord messages

• semi-structured data lie JSON logs, XML configs

• other formats: like spreadsheets, audio transcripts, images

Traditionally, finding the right answer inside all this is painful:

• you need SQL skills or BI dashboards for structured data

• keyword search often fails on unstructured docs

• information is stuck in silos

• data gets outdated fast

This means knowledge exists, but access is slow, fragmented, and frustrating.

Where LLMs Help and Where They Fail

Large Language Models (LLMs) like GPT or Claude seem like a solution.

You ask a question in plain English, and they give you an answer.

But by themselves, LLMs are risky for enterprise use:

• they only know what they were trained on (no company-specific data)

• they can make up answers that sound right but aren’t thus hallucinations

• you can’t dump millions of documents into them thus context limit

• you can’t just hand sensitive company data to public models thus data privary

• they don’t show where the answer came from thus citations.

So we need something better.

Retrieval-Augmented Generation

RAG is an architecture that grounds LLMs in real, up-to-date knowledge.

It works in three steps:

1) Retrieval

Search your knowledge base (internal docs, databases, real-time feeds) for the most relevant pieces of information.

2) Augmentation

Merge that retrieved info with the user’s query to create a richer, well grounded prompt.

3) Generation

The LLM uses this prompt to generate a clear, natural language answer with the retrieved facts.

Think of it like augmented reality for knowledge.

Just as AR overlays digital objects on the real world.

RAG overlays an LLM’s reasoning on top of your company’s trusted data.

How RAG Works in Practice

Here’s the typical pipeline:

ingest & index data -> handle query -> build prompt -> generate the answer

1) Ingest & Index Data

• collect documents from CRMs, file systems, APIs

• clean and preprocess them

• break big documents into chunks (paragraphs or sections)

• convert chunks into embeddings (vector representations)

• store them in a vector database (like pinecone, weaviate, pgvector, faiss, chroma)

2) Handle Query

• user asks - “What are the key changes in our Q3 renewable energy report?”

• the query is also embedded into a vector

• the system finds the most semantically similar chunks in the vector DB

3) Build Prompt

• combine the query + top retrieved chunks into a single structured prompt

• keep within the LLM’s context window

• explicitly tell the LLM - “answer only from the provided context and cite sources”

4) Generate Answer

• LLM synthesizes a response using only the grounded info

• return the answer + source citations

Why RAG Matters for Professionals

For leaders, PMs, and VPs:

• always pulls from the latest data

• cuts hallucinations by grounding answers in trusted sources

• no need to retrain giant models every time data changes

• answers come with citations, which is critical in legal, medical, or financial contexts

• teams spend less time searching and more time acting

For engineers and developers:

• swap or add new data sources without retraining

• keep sensitive data in your own vector DB, not in the model

• skills in chunking, embeddings, vector search, and orchestration are more valuable than prompt tricks

• context windows don’t limit you anymore; retrieval narrows it down

Challenges You Can’t Ignore

RAG isn’t magic (obviously).

To make it work, teams must get the details right:

• data quality

• chunking

• embedding model choice

• vector DB performance

• access control

• monitoring

Final Thought

RAG is how you unlock real business value from LLMs.

Instead of retraining giant models, you:

• keep a clean, searchable knowledge base

• retrieve the right information

• feed it to the model

• get accurate, current, cited answers

It’s pragmatic, scalable, and enterprise-ready.

The companies that master RAG are the ones that will turn AI from a demo into a dependable decision engine.

We do have a product in this area, it’s an on-premises knowledge engine for enterprises.

Let’s Talk

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