RAG Architecture Engineering
Retrieval-Augmented Generation (RAG) is the enterprise standard for building reliable AI. We engineer advanced RAG pipelines—moving past basic LangChain tutorials to implement hybrid search, query rewriting, and semantic caching—ensuring the AI retrieves the exact right document and delivers factually bulletproof answers.
Core Features
Advanced Chunking Strategies
Moving beyond naive text splitting. We use semantic chunking and hierarchy-aware parsing to preserve the context of complex PDFs and tables.
Hybrid Search (Vector + BM25)
Combining the semantic understanding of Vector embeddings with the exact-keyword accuracy of BM25 (Elasticsearch) to ensure maximum retrieval recall.
Query Rewriting & Routing
Using an LLM to intercept the user's messy query, clean it, expand the vocabulary, and route it to the correct specialized database.
Re-ranking Algorithms
Implementing Cross-Encoders (like Cohere Rerank) to meticulously score and re-order the retrieved documents before sending them to the final LLM.
Our Process
Data Ingestion & Parsing
Week 1-2Building pipelines to extract text from your specific data sources (SharePoint, Notion, messy PDFs) using advanced OCR and layout detection.
Embedding & Vector DB Setup
Week 3Testing various embedding models (OpenAI, Cohere, BGE) to find the best domain fit, and indexing the chunks into a high-performance vector store.
Advanced Retrieval Logic
Week 4-5Developing the hybrid search queries, self-querying retrievers, and integrating the Cross-Encoder re-ranking step for maximum accuracy.
Generation & Citation
Week 6Prompt engineering the final LLM to synthesize the retrieved context and strictly generate responses that include verifiable footnotes/citations.
RAG Evaluation (RAGAS)
Week 7-8We don't guess if it works. We use frameworks like RAGAS to mathematically score the pipeline on Context Precision, Recall, and Answer Faithfulness.
Technologies We Use
FAQ
Why is the AI giving me the wrong answer even with RAG?
Can RAG read tables and charts in PDFs?
How do you measure if the RAG system is actually good?
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