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ZyVOP2h ago · 11 min read

Choosing a Vector Database: pgvector vs Dedicated Stores

The real question isn't which vector database is best. It's where your vectors should live: next to the rest of your data in Postgres, or in a system built only for similarity search. Many teams alrea

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Elay osetrov7h ago · 3 min read

Can user A's question pull user B's documents? A tenant-isolation checklist for RAG

Most RAG data leaks don't need a clever attacker. They need one missing filter. A shared vector index, a retrieval call that forgot the tenant_id, a semantic cache keyed only by the question text, and

setrov.hashnode.dev

Ashen Jayawardena8h ago · 23 min read

The Tester's Guide to RAG Evaluation, Part 2: Building the Evals

In Part 1 you met the RAG system: retrieval finds passages in a corpus, generation writes an answer from them, and each half fails in its own way. You spiked the system to see how it really behaves, a

ashenasserts.hashnode.dev

Magenta Waterwheel9h ago · 7 min read

Converting PDFs to Clean Markdown for RAG and LLM Pipelines

If you've built a RAG pipeline that ingests real-world documents instead of clean .txt files, you've hit this problem: PDFs don't contain text the way you'd want them to. They contain layout instructi

magentawaterwheel.hashnode.dev

Ashen Jayawardena13h ago · 22 min read

The Tester's Guide to RAG Evaluation, Part 1: Foundations

If you test software, you already ask the question that matters most in AI evaluation: how would I know this is wrong? Now ask it of a system that answers questions from a company's documents. The sam

ashenasserts.hashnode.dev

Rounak Show17h ago · 8 min read

Understanding LLM Embeddings from Scratch — Part 5: Word2Vec

In Module 4, we treated embeddings as raw parameters—numbers in a matrix tuned by backpropagation to minimize a loss value. Now, we step back from the raw mechanics to look at the emergence of meaning

rounakshow.hashnode.dev

RONI DAS21h ago · 8 min read

How to Troubleshoot Jenkins Pipeline Failures Using RAG and AI

#DevOps #Jenkins #Artificial Intelligence #RAG #AIOps Have you ever spent 30 minutes or even a few hours troubleshooting a Jenkins pipeline failure, only to discover that your team had already fixed t

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TechTIQ Inc1d ago · 4 min read

RAG vs Fine-Tuning vs Prompt Engineering: Choosing the Right Approach

Teams building with large language models usually ask the same question in the first week: should we prompt it better, give it our documents (RAG), or fine-tune it? Each approach solves a different pr

techtiqinc.hashnode.dev

Rounak Show1d ago · 10 min read

Understanding LLM Embeddings from Scratch — Part 4: How Does an Embedding Actually Learn?

So far, we've learned why embeddings exist, how vectors, dimensions, and similarity work, how nn.Embedding stores vectors and retrieves them using token IDs, how padding and attention masks handle var

1 rounakshow.hashnode.dev

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Trending tags this week

Choosing a Vector Database: pgvector vs Dedicated Stores

Can user A's question pull user B's documents? A tenant-isolation checklist for RAG

The Tester's Guide to RAG Evaluation, Part 2: Building the Evals

Converting PDFs to Clean Markdown for RAG and LLM Pipelines

The Tester's Guide to RAG Evaluation, Part 1: Foundations

Understanding LLM Embeddings from Scratch — Part 5: Word2Vec

More Chunks Found the Right Document 90% of the Time. The Model Used It Less. RAG Interview Questions, With the Numbers

How to Troubleshoot Jenkins Pipeline Failures Using RAG and AI

RAG vs Fine-Tuning vs Prompt Engineering: Choosing the Right Approach

Understanding LLM Embeddings from Scratch — Part 4: How Does an Embedding Actually Learn?

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Explore Hashnode

Trending tags this week

Choosing a Vector Database: pgvector vs Dedicated Stores

Can user A's question pull user B's documents? A tenant-isolation checklist for RAG

The Tester's Guide to RAG Evaluation, Part 2: Building the Evals

Converting PDFs to Clean Markdown for RAG and LLM Pipelines

The Tester's Guide to RAG Evaluation, Part 1: Foundations

Understanding LLM Embeddings from Scratch — Part 5: Word2Vec

More Chunks Found the Right Document 90% of the Time. The Model Used It Less. RAG Interview Questions, With the Numbers

How to Troubleshoot Jenkins Pipeline Failures Using RAG and AI

RAG vs Fine-Tuning vs Prompt Engineering: Choosing the Right Approach

Understanding LLM Embeddings from Scratch — Part 4: How Does an Embedding Actually Learn?