Retrieval-Augmented Generation (RAG) performance is bounded by the quality of the context supplied to the LLM. If your retrieval step introduces irrelevant documents (false positives), the language model either hallucinates or produces diluted, unfocused answers.…
Chunking Strategies for Complex PDFs: Dealing with Tables and Multi-Column Layouts in RAG
Naive chunking algorithms (like fixed-length character splitters with 500 characters and 50 overlap) are the primary reason enterprise Retrieval-Augmented Generation (RAG) pipelines fail when deployed against real-world PDF documents. When a standard text splitter parses a PDF with two-column financial …
Hybrid Search Explained: Combining BM25 Keyword Search and Vector Embeddings in RAG
When building enterprise Retrieval-Augmented Generation (RAG) systems in 2026, relying purely on dense vector embeddings (like OpenAI text-embedding-3 or BGE) introduces severe blind spots. Dense vectors excel at semantic similarity—understanding that ‘laptop’ and ‘notebook computer’ mean the same thing. However, …
Building Production RAG with PostgreSQL and pgvector in Python
Retrieval-Augmented Generation (RAG) is the industry standard for grounding Large Language Models in private domain data. While specialized vector databases like Pinecone, Chroma, and Qdrant are popular, managing a separate vector database creates data sync headaches, dual authentication configurations, and …
SQL Where Clause – Short note
In this short note, Listed some quick relevant WHERE Clause keywords and methods which will use in SQL query.
WHERE clause is used to add condition(s), so it returns data satisfying provided condition.
SELECT lastname, firstname, title FROM employee WHERE …
SQL Window Function – part 1
Hi folks here in this article we are going to see what is window function in SQL, and how it is useful for data analyst for data preprocessing or generation accumulated data with lots of information, combination of calculation and …