Retrieval-Augmented Generation
Grounded Q&A systems, hybrid vector-keyword retrieval pipelines, context re-ranking, and hallucination reduction.
1 / Why RAG Matters
Retrieval-Augmented Generation solved a fundamental limitation of large language models: they hallucinate when asked about information outside their training data. Rather than fine-tuning models on proprietary data — expensive, slow, and inflexible — RAG retrieves relevant context at query time and feeds it into the model's context window alongside the user's question.
2 / Building RAG Pipelines
My primary RAG implementation was Phoenix, a hybrid retrieval engine that combined dense vector search via pgvector with sparse keyword matching via BM25. The pipeline followed a multi-stage architecture: query rewriting → parallel retrieval (semantic + lexical) → score fusion → Reranking → LLM generation. Each stage was instrumented to expose step-by-step scoring math, eliminating the black-box problem where you cannot tell why a model produced a specific answer.
StudyLink implemented a simpler RAG variant — Semantic Search over educational documents using Vector Embeddings stored in PostgreSQL with pgvector. The simpler pipeline was appropriate for the use case, where document similarity matching was sufficient without the complexity of Hybrid RAG.
3 / The Hybrid Approach
Pure vector search misses exact keyword matches — a query for 'BM25 algorithm' might retrieve documents about 'ranking algorithms' but miss the exact term match. Pure keyword search misses semantic meaning — a query for 'document relevance scoring' would miss articles about 'ranking' that use different terminology. Hybrid RAG combines both approaches, and the engineering challenge is merging their fundamentally different score scales through fusion techniques like Reciprocal Rank Fusion.
4 / Retrieval Traceability
One principle I committed to in Phoenix was retrieval traceability. Every answer the system produced came with the retrieval chain that led to it — which documents were retrieved, what scores they received, why the reranker promoted or demoted them. This transparency is not just a debugging tool; it is the difference between a system that bluffs and a system that shows its work.
5 / Where My Understanding Stands
I have built functional RAG pipelines, but my experience is concentrated in the retrieval and fusion stages rather than advanced techniques like iterative retrieval, multi-hop reasoning, or retrieval-augmented fine-tuning. The systems I built work — Phoenix demonstrates transparent hybrid retrieval with traceable scoring — but there is significant depth in the RAG research space that I have not explored.