The GigaRAG blog

Guides on retrieval-augmented generation, hybrid search, embeddings, and building AI systems that answer from your own data.

Retrieval

What Re-Embedding a Large Corpus Actually Costs

GigaRAG teamSeptember 19, 202617 min read

Editorial overhead workbench scene showing document cards moving through an embedding module into vector cards, with a cost meter and separate trays for engineering hours and vector database migration costs, illustrating the true cost of re-embedding a large corpus for GigaRAG.

Retrieval

No Compactable Messages Available: Causes and Fixes

GigaRAG teamSeptember 19, 202620 min read

A developer at a workbench sees a no compactable messages available error on a laptop while moving conversation cards toward an external memory drive instead of a stalled compaction funnel.

Retrieval

Context Left Until Auto-Compact: A Guide for RAG Builders

GigaRAG teamSeptember 19, 202622 min read

GigaRAG editorial illustration of a Claude Code terminal showing the context left until auto-compact indicator, with a developer's hand routing conversation state to an external memory drive and vector database cylinder.

Retrieval

Retrieval Strategies Explained for RAG Pipelines

GigaRAG teamSeptember 18, 202618 min read

Retrieval

How a RAG Pipeline Works: A Practical Guide

GigaRAG teamSeptember 18, 202617 min read

Editorial workbench showing a RAG pipeline for agent memory: overlapping document chunks flow through an embedding module into vector cards, a vector database, a reranker, and a laptop, with a write-back arrow returning to the database.

Retrieval

Incremental Indexing and Re-Embedding Without Downtime

GigaRAG teamSeptember 18, 202613 min read

Editorial workbench with a dual-index switching board and two vector database cylinders, showing changed document cards flowing into a new index while a live query interface stays active, illustrating incremental indexing without downtime for GigaRAG.

Retrieval

Ingesting HTML, Notion, and Slack for RAG Pipelines

GigaRAG teamSeptember 18, 202619 min read

Retrieval

Reranking Models Compared: NDCG, Latency, Cost

GigaRAG teamSeptember 18, 202624 min read

Retrieval

Query Rewriting and Expansion: A Decision Framework for RAG

GigaRAG teamSeptember 18, 202624 min read

Retrieval

What Reranking Does and Why It Exists in RAG

GigaRAG teamSeptember 18, 202626 min read

Retrieval

What Embeddings Actually Encode: A Guide for RAG Builders

GigaRAG teamSeptember 16, 202615 min read

Editorial workbench showing vector space clusters for bank meanings and close placement of not good and good, illustrating what embeddings encode and lose in RAG and agent memory.

Retrieval

Chunking Explained Without the Jargon: A Plain Guide

GigaRAG teamSeptember 16, 202623 min read

Retrieval

RAG Architecture in LangChain vs LlamaIndex vs Custom

GigaRAG teamSeptember 16, 202620 min read

GIGARAG

Retrieval

Sync vs Async Ingestion Architecture for RAG and Agent Memory

GigaRAG teamSeptember 16, 202614 min read

GIGARAG

Retrieval

What Reranking Does in RAG Pipelines

GigaRAG teamSeptember 16, 202625 min read

Overhead editorial workbench showing a reranker module reordering five retrieved document cards before they enter a laptop, with a small clock and compute chip marking latency and cost.

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