Oshan Ranasinghe

Work / AI / ML / RAG Knowledge Assistant

RAG · LLM Application

RAG Knowledge Assistant

A Retrieval-Augmented Generation system that ingests documents, builds a vector index, and answers domain-specific questions with cited source passages.

Section

AI / ML

Stack

RAG · LangChain · ChromaDB · OpenAI

Category

RAG · LLM Application

Status

Pipeline complete, evaluation and chunking tuning ongoing

01 — Challenge

The core engineering challenge

LLMs hallucinate confidently on domain-specific questions — grounding generation in retrieved evidence with explicit citations shifts the failure mode from silent hallucination to visible retrieval gaps.

03 — Decisions

Engineering decisions and what they cost

01Chunk documents by semantic boundary rather than fixed token count to preserve context across splits.
02Return source passages alongside generated answers so users can verify every claim.
03Separate retrieval quality evaluation from generation quality — they fail in different ways.

04 — Stack

RAGLangChainChromaDBOpenAIEmbeddingsPython