A practical guide to designing RAG systems. Covers document ingestion, chunking, embeddings, vector search, reranking, prompt composition, and evaluation from a real product engineering perspective.
RAG quality starts with data, not the model. This post explains how to choose source documents, clean HTML/PDF/wiki data, attach metadata, and build a production-ready ingestion pipeline.
Chunking and embeddings define the floor of retrieval quality. This post covers chunk size, overlap, heading preservation, code block handling, embedding model selection, and indexing strategy.
Search quality largely defines RAG quality. This post explains dense retrieval, BM25, hybrid search, query rewriting, metadata filtering, and reranking from a practical engineering perspective.
Retrieval is only half of RAG. This post explains how to structure prompts, select and compress context, design citations, and make the system answer safely when evidence is weak.
To move RAG into production, you need quality evaluation, logging, latency tracking, and feedback loops. This post covers retrieval metrics, groundedness, citation accuracy, observability, and operational checklists.
A practical blueprint for a RAG-based AI stock investment Agent. Covers product goals, user scenarios, system boundaries, core components, and end-to-end architecture for a research and paper-trading workflow.
A practical guide to building the RAG data layer for an AI stock investment Agent. Covers price data, news, SEC filings, earnings transcripts, normalization, chunking, metadata, and freshness-aware retrieval.
A practical design for the workflow of an AI stock investment Agent. Covers routing, query parsing, screening, retrieval analysis, quantitative analysis, risk evaluation, and final report composition.
A practical implementation blueprint for a RAG-based stock investment Agent using FastAPI, PostgreSQL, pgvector, Redis, async workers, and domain-separated service modules.