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repeatable.

Why We Built DataFramer

We kept hitting the same wall. Here is what it was and what we built to fix it.

An Overview of Retrieval-Augmented Generation (RAG) and Its Different Components

RAG components, retrieval strategies, and how to build systems that ground LLM outputs in real data.

Top Problems with RAG Systems and Ways to Mitigate Them

The most common RAG failure modes and the best practices that address each one.

LLM-as-Judge: Why It's Hard to Get Right and Why It Still Matters

When LLM-as-judge works, when it breaks down, and what it actually takes to build one you can trust.

A Quick Comparison of Vector Databases for RAG Systems

ApertureDB, Pinecone, Weaviate, and Milvus compared on features, performance, and RAG use cases.

A Practical Guide to Agentic LLM Frameworks

A practical overview of agentic LLM frameworks: reasoning, planning, tool use, and the real challenges of running them in production.

How to Fix Hallucinations in RAG LLM Apps

Concrete techniques for diagnosing and reducing hallucinations in RAG-based LLM applications.

An Expert's Guide to Picking Your LLM Tech Stack

A practical breakdown of every layer in the LLM stack: models, orchestration, storage, and ops.

Top Strategies for Detecting LLM Hallucinations

Detection strategies for hallucinations in both RAG and non-RAG LLM applications, and the real tradeoffs between them.